Proceedings of the 12th European Conference on E-Learning volume Two ECEL 2013 by ACPIL

Proceedings of the 12th European Conference on e Learning e-Learning SKEMA Business School Sophia Antipolis France 30-31 October 2013 Volume Two

Edited by MĂŠlanie Ciussi and Marc Augier

A conference managed by ACPI, UK www.academic-conferences.org

Proceedings of the 12th European Conference on e-Learning ECEL 2013 SKEMA Business School Sophia Antipolis, France 30-31 October 2013 Edited by MĂŠlanie Ciussi and Marc Augier

Copyright The Authors, 2013. All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors. Papers have been double-blind peer reviewed before final submission to the conference. Initially, paper abstracts were read and selected by the conference panel for submission as possible papers for the conference. Many thanks to the reviewers who helped ensure the quality of the full papers. These Conference Proceedings have been submitted to Thomson ISI for indexing. Please note that the process of indexing can take up to a year to complete. Further copies of this book and previous yearâ&#x20AC;&#x2122;s proceedings can be purchased from http://academic-bookshop.com E-Book ISBN: 978-1-909507-84-5 E-Book ISSN: 2048-8645 Book version ISBN: 978-1-909507-82-1 Book Version ISSN: 2048-8637 CD Version ISBN: 978-1-909507-85-2 CD Version ISSN: 2048-8637

Published by Academic Conferences and Publishing International Limited Reading UK 44-118-972-4148 www.academic-publishing.org

Contents Paper Title

Author(s)

Page No.

Preface

Committee

Biographies

When Computers Will Replace Teachers and Counsellors: Heaven and Hell Scenarios

Aharon (Roni) Aviram and Yoav Armony

Planning and Implementing a new Assessment Strategy Using an e-Learning Platform

Rosalina Babo and Ana Azevedo

Authentic Learning in Online Environments – Transforming Practice by Capturing Digital Moments

Wendy Barber, Stacey Taylor and Sylvia Buchanan

Signature Based Credentials, an Alternative Method for Validating Student Access in e-Learning Systems

Orlando Belo, Paulo Monsanto and Anália Lourenço

Two-way Impact: Institutional e-Learning Policy/Educator Practices in Creative Arts Through ePortfolio Creation

Diana Blom, Jennifer Rowley, Dawn Bennett, Matthew Hitchcock and Peter Dunbar-Hall

Automated Evaluation Results Analysis With Data Mining Algorithms

Farida Bouarab-Dahmani and Razika Tahi

Language e-Learning Based on Adaptive Decision-Making System

Vladimír Bradáč and Cyril Klimeš

Barriers Engaging With Second Life: Podiatry Students Development of Clinical Decision Making

Margaret Bruce, Sally Abey, Phyllis Waldron and Mark Pannell

Tasks for Teaching Scientific Approach Using the Black Box Method

Martin Cápay and Martin Magdin

Blended Learning as a Means to Enhance Students’ Motivation and to Improve Self-Governed Learning

Ivana Cechova and Matthew Rees

Strategies for Coordinating On-Line and Face-To-Face Components in a Blended Course for Interpreter Trainers

Barbara Class

iBuilding for Success? iBooks as Open Educational Resources in Built Environment Education

David Comiskey, Kenny McCartan and Peter Nicholl

Facilitation of Learning in Electronic Environments: Reconfiguring the Teacher’s Role

Faiza Derbel

Effect of e-Learning on Achievement and Interest in Basic General Mathematics Among College of Education Students in Nigeria

Foluke Eze

101

Self-Organization of e-Learning Systems as the Future Paradigm for Corporate Learning

Gert Faustmann

106

An Online Tool to Manage and Assess Collaborative Group Work

Alvaro Figueira and Helena Leal

112

Design 4 Pedagogy (D4P): Designing a Pedagogical Tool for Open and Distance Learning Activities

Olga Fragou and Achilles Kameas

121

The Affordances of 4G Mobile Networks Within the UK Higher Education Sector

Elaine Garcia, Martial Bugliolo, and Ibrahim Elbeltagi

131

An Integral Approach to Online Education: An Example

Jozef Hvorecky

139

Paper Title

Author(s)

Page No.

Scaffolding in e-Learning Environment

Antonín Jančařík

149

Planning for Success in Introducing and Embedding Technology to Enhance Learning

Amanda Jefferies and Marija Cubric

156

Adopting Blended Learning – Practical Challenges and Possible Solutions for Small Private Institutions

Olga Kandinskaia

164

Evaluation of e-Learning Courses for Lifelong Learning

Jana Kapounova, Milan Majdak and Pavel Novosad

173

Interuniversity Collaborative Learning With Wiki Toolsets

Elisabeth Katzlinger and Michael Herzog

184

Something for Everyone: MOOC Design for Informing Dementia Education and Research

Carolyn King, Jo-Anne Kelder, Rob Phillips, Fran McInerney, Kathleen Doherty, Justin Walls, Andrew Robinson and James Vickers

191

Collaborative Learning Environment for Discussing Topic Explanation Skill Based on Presentation Slide

Tomoko Kojiri, Hayato Nasu, Keita Maeda, Yuki Hayashi and Toyohide Watanabe

199

Learning Potentials of e-Assessments: Developing Multiple Literacies Through Media Enhanced Assessment

Christopher Könitz, Jakob Diel and Jürgen Cleve

209

Methodology for Creating Adaptive Study Material

Kateřina Kostolányová and Jana Šarmanová

218

Using Twitter, Blogs and Other Web 2.0 Technologies and Internet Resources to Enhance Arabic as a Foreign-Language Reading Skills

Blair Kuntz

224

The use of Social Networks by Universities for Communication at Institutional Level

Wolfram Laaser, Julio Gonzalo Brito and Eduardo Adrián Toloza

231

Developing Active Collaborative e-Learning Framework for Vietnam’s Higher Education Context

Long Le, Hao Tran and Axel Hunger

240

Telepresence as Educational Practice in the Third TeachingRoom – a Study in Advanced Music Education

Karin Tweddell Levinsen, Rikke Ørngreen and Mie Buhl

250

An Empirical Study on Faculty Perceptions and Teaching Practices of Wikipedia

Josep Lladós, Eduard Aibar, Maura Lerga, Antoni Meseguer and Julià Minguillon

258

How to Motivate Adult Learners Through e-Learning: Some key Insights From Research Case Studies

Kevin Lowden, Rahela Jurković and Peter Mozelius

266

Training Teachers to Learn by Design, Through a Community of Inquiry

Katerina Makri, Kyparisia Papanikolaou, Athanasia Tsakiri and Stavros Karkanis

274

Usefulness of Feedback in e-Learning From the Students’ Perspective

María-Jesús Martínez-Argüelles, Dolors PlanaErta, Carolina Hintzmann-Colominas, Marc Badia-Miró and Josep-Maria Batalla-Busquets

283

Trust as an Organising Principle of e-Learning Adoption: Reconciling Agency and Structure

Jorge Tiago Martins and Miguel Baptista Nunes

293

Smart Environments for Learning – Multi-Agent Systems Approach

Peter Mikulecky

304

Assessment of Virtual Learning Environments by Higher Education Teachers and Students

Luísa Miranda, Paulo Alves and Carlos Morais

311

Learning by Building – the Lunarstorm Generation Constructing Their own ePortfolios

Peter Mozelius

319

Learning and Instruction in the Digital Age

Antoinette Muntjewerff

323

Paper Title

Author(s)

Page No.

Effectiveness of Instructional Suggestions for Note-Taking Skills in a Blended Learning Environment

Minoru Nakayama, Kouichi Mutsuura and Hiroh Yamamoto

333

Evaluation of Massive Open Online Courses (MOOCs) From the Learner’s Perspective

Bernard Nkuyubwatsi

340

In the Presence of Technology – Teaching in Hybrid Synchronous Classrooms

Anne-Mette Nortvig

347

Searching for the Ideal CLIL Course Design

Jarmila Novotná and Lenka Procházková

354

[Teaching Desktop] Video Conferencing in a Collaborative and Problem Based Setting

Rikke Ørngreen and Per Mouritzen

360

Challenging Pre-Service Teachers’ on Collaborative Authoring of Learning Designs in a Blended Learning Context

Kyparisia Papanikolaou and Evangelia Gouli

369

Technology-Enhanced-Learning and Student-Centeredness in a Foreign Language Military Class – a Case Study

Maria-Magdalena Popescu, Ruxandra Buluc, Luiza-Maria Costea and Speranza Tomescu

378

The Disruptive Potential of e-Learning in Academe and Beyond: A Futuristic Perspective

Ali Raddaoui

386

What Really Happens When Educators Make and Evaluate TEL Innovations?

Claire Raistrick

393

A Reality Check on Student Mobile Adoption and Content Creation in Resource-Constrained Environments

Patient Rambe and Liezel Nel

401

Student Perceptions on the Usefulness of Educational Technologies at a South African University

Patient Rambe and Liezel Nel

411

Digital Services Governance With AGIMUS

David Reymond

420

Functional Architecture of a Service-Oriented Integrated Learning Environment

Danguole Rutkauskiene, Rob Mark, Ramunas Kubiliunas and Daina Gudoniene

431

Using Social Network VKontakte for Studying Sociology

Daniyar Sapargaliyev and Assel Jetmekova

440

Automatic Creation of Semantic Network of Concepts in Adaptive e-Learning

Emilie Šeptáková

447

Gathering the Voices: Disseminating the Message of the Holocaust for the Digital Generation

Angela Shapiro, Brian McDonald and Aidan Johnston

457

Monitoring the Concept of e-Learning in Mind Maps of University Students

Ivana Šimonová

463

Impact of Internet Usage on Students’ Academic Performance

Florica Tomos, Christopher Miller, Paul Jones, Ramdane Djebarni, Oshisanya Oluwaseyi Olubode, Peter Obaju-Falade, Henrietta Eleodimuo Nkiruka and Tejaswi Asmath

470

An International Approach to Creative Pedagogy and Students’ Preferences of Interactive Media

Florica Tomos, Peter Mozelius, Olga Shabalina, Oana Cristina Balan, Christos Malliarakis, Christopher Miller, David Turner and Paul Jones

479

The Influence of the “Approach gap” Between Students’ and Teachers’ e-Learning Preferences

Nazime Tuncay

488

Tutoring and Automatic Evaluation of Logic Proofs

Karel Vaculík, Lubomír Popelínský, Eva Mráková and Juraj Jurčo

495

The Global Classroom Video Conferencing Model and First Evaluations

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen

503

iii

Paper Title

Author(s)

Page No.

Social Media as an Educational Tool: Students’ Perspectives and Usage

Jan Wiid, Michael Cant and Corinne Nell

511

Teaching GHG Reduction for the Food Industry to Adult Learners Using Blended Learning

Stephen Wilkinson, Duncan Folley, Cathy Barnes, Philip Richard Scott and Quintan Thornton

521

E-Learning and Life-Long Learning: A Descriptive Case Study From a Teacher Educator’s Perspective: 1995-2013

Eleanor Vernon Wilson

531

Can e-Learning Identify Poor Performers in Medical School?

Hitomi Yukawa, Raoul Breugelmans, Takashi Izumi and Miki Izumi

537

A Novel Approach to e-Learning: Yasar University e-Learning System (YES)

Ibrahim Zincir, Melih Zeytinoglu, Ahmed Rana and Samsun Basarici

546

PHD Papers

553

Cultural Differences in Students’ Perceptions Towards Online Learning Success Factors

Armando Cortés and Elena Barbera

555

Visual Analytics by Animations in Higher Education

Jan Géryk

565

Strategies for Digital Inclusion - Towards a Pedagogy for Embracing Student Diversity With Online Learning

Baylie Hart Clarida, Milena Bobeva, Maggie Hutchings and Jacqui Taylor

573

GeoGebra in Teaching Linear Algebra

Veronika Havelková

581

E-Learning Based Preparation for Educational Activities Outside of School

Jiří Hoffman

590

Machine and Social Intelligent Peer-Assessment Systems for Assessing Large Student Populations in Massive Open Online Education

Cristian Jimenez-Romero, Jeffrey Johnson and Ricardo De Castro

598

Virtual Guide as a Means of a Tailored Tour of an Educational Exhibition

Lukas Najbrt

608

Online Interactive Module for Teaching a Computer Programming Course

Aisha Othman, Crinela Pislaru and Ahmed Impes

617

The Highs and Lows of Ubiquitous Mobile Connectivity Investigating Students' Well-Being

Michele Salvagno

626

Non Academic Papers

635

Development of a Fully Integrated Global Learning System in a Regulated Environment

Chuck Sigmund, Doug Wallace and Terry Kliever

637

PAOK – ICT Network for Upper Secondary Education

Riikka Vanninen, Matleena Laakso and Minna Helynen

643

Work In Progress Papers

647

Challenges in Medical Education by e-Learning

Elena Taina Avramescu, Dorin Popescu, George Ionescu and Georgios Antonopoulos

649

Activity-Based Choice of Connection and Device in e/mLearning

Cristina De Castro

354

The Digital Carrot and Survival Stick for Increased Learning and Teaching Agility

Sue Greener and Piers MacLean

659

Paradigm Shift - Engaging Academics in Social Media - the Case of Bournemouth University

Irma Kalashyan, Diyana Kaneva, Sophie Lee, David Knapp, Gelareh Roushan and Milena Bobeva

662

Paper Title

Author(s)

Page No.

A Global Approach to Graduate Education and Research Training

Barbara Moser-Mercer and Barbara Class

666

OLAREX: Initiating Secondary Schools Teachers Into Online Labs Experience For Teaching

Ramona Georgiana Oros, Andreas Pester and Olga Dziabenko

670

Promoting Staff Engagement With Social Networking in Higher Education

Rebecca Rochon and John Knight

673

Preface

These Proceedings represent the work of contributors to the 12th European Conference on e-Learning, ECEL 2013, hosted this year by SKEMA Business School, Sophia Antipolis, France. The Conference Chair is Dr Mélanie Ciussi, and the Programme Chair is Dr Marc Augier, both from SKEMA Business School, Sophia Antipolis, France. The conference will be opened with a keynote address by Prof Steven Warburton, Head of Department of Technology Enhanced Learning, University of Surrey, UK, on the topic of“Uncertain futures: adapting to rapid change through patterns and analytics“. The second day will be opened by Dr Viktor Dörfler, Director of the Management Development Programme, Management Science Department, University of Strathclyde Business School, Glasgow, United Kingdom on the topic of "Passionate Learners: Lifelong Learning in a Flux". As usual the papers range across a very wide spectrum of issues, all of which are pertinent to the successful use of e-Learning applications. It is clear that the role being played by e-Learning in the pedagogical process is considerable and that there is still ample scope for further development in this area. The ECEL Conference constitutes a knowledge hub for individuals to present their research findings, display their work in progress and discuss conceptual advances in many different branches of e-Learning. At the same time, it provides an important opportunity for members of the EL community to come together with peers, share knowledge and exchange ideas. With an initial submission of 160 abstracts, after the double blind, peer review process there are 68 academic papers, 9 Phd Papers, 7 Work in Progress papers and 2 non academic papers in these Conference Proceedings. These papers reflect the truly global nature of research in the area with contributions from Australia, Austria, Canada, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Israel, Italy, Japan, Kazakhstan, Lithuania, Nigeria, Portugal, Romania, Slovakia, South Africa, Spain, Sweden, Switzerland, The Netherlands, Tunisia, Turkey, UK, USA, and Vietnam. A selection of papers – those agreed by a panel of reviewers and the editor will be published in a special conference edition of the EJEL (Electronic Journal of e-Learning www.ejel.org ). I wish you a most interesting conference. Mélanie Ciussi, Conference Chair and Marc Augier Programme Chair October 2013

ConferenceCommitee Conference Executive Dr Mélanie Ciussi, SKEMA Business School, Sophia Antipolis, France Mini track Chairs Dr Mélanie Ciussi, SKEMA Business School, Sophia Antipolis, France Jorge Tiago Martins, University of Sheffield, UK Dr Jana Kapounova, University of Ostrava, Czech Republic Prof. Ali H. Raddaoui, University of Wyoming in Laramie , USA Dr Kim C. Long, Wiley College, Texas, USA Dr Rikke Orngreen, Aalborg University, Denmark Dr Marc Augier, SKEMA Business School, Sophia Antipolis, France Committee members The conference programme committee consists of key people in the e-learning community around the world. The following people have confirmed their participation: Ariffin Abdul Mutalib (Universiti Utara Malaysia, Malaysia); Dr. Siti aishah Abdullah (University Technology Mara, Kelantan, Malaysia); Babajide Abidogun (Faculty of Education, University of Plymouth, South Africa); Dr Wilfried Admiraal (Leiden University, Leiden, The Netherlands); Associate Professor Dr Zainal Abidin Akasah (Universiti Tun Hussein Onn Malaysia, Malaysia); Dr Ali Alawneh (Philadelpia University, Jordan); Shafqat Ali (University of Western Sydney, Australia); Prof. Dr. Maizam Alias (Universiti Tun Hussein Onn, Malaysia); Professor, Dr Abdallah Al-Zoubi (Princess Sumaya University for Technology, Jordan); Prof Antonios Andreatos (Hellenic Air Force Academy, Greece); Dr. Anca-Olga Andronic (Faculty of Psychology and Educational Sciences, Spiru Haret University, Romania); Dr. Razvan-Lucian Andronic (Spiru Haret University, Romania); Dr. Alla Anohina (Riga Technical University, Latvia); Sara Archard (University of Waikato, Hamilton, New Zealand); Ezendu Ariwa (London Metropolitan University, Uk); Professor Mohamed Arteimi (Libyan Academy of Graduate studies, Tripoli, Libya); Dr William Ashraf (University of Sussex, UK); Dr Bunyamin Atici (Firat University, Turkey); Marc Augier (SKEMA Business School , France); Stephanos Avakian (Brighton Business School, University of Brighton,, UK); Dr Anders Avdic (Orebro University, Sweden); Simon Bachelor (Gamos, Reading, UK); Prof Alina Badulescu (University of Oradea, Romania); Dr Nimalathasan Balasundaram (University of Jaffna, Sri Lanka); Dr Joan Ballantine (University of Ulster, UK); Dr Trevor Barker (University of Hertfordshire, UK); Dr Josep-Maria Batalla (Universitat Oberta de Catalunya, Spain); Catherine Beaton (Rochester Institute of Technology, USA); Hans J.A Beldhuis (University of Groningen, The Netherlands); Professor Orlando Belo (University of Minho Campus de Gualtar, Portugal); Dr David Benito (Public University of Navarre, Pamplona, Spain); Andrea Benn (University of Brighton, UK,); Yongmei Bentley (University of Bedfordshire, UK); Daniel Biella (University of Duisburg-Essen, Germany); Dr Radu Bilba (George Bacovia University,, Romania); Eric Bodger (University of Winchester, UK); Dr. Tharrenos Bratitsis (University of Western Macedonia, Greece); Dr Ann Brown (CASS Business School, London, UK); Dr Mark Brown (Massey University, Palmerston North, New Zealand); Mel Brown (Plymouth College of Art, UK); Giuseppe Cannavina (University of Sheffield, UK); James Carr (University of Newcastle, UK); Maggie Carson (Edinburgh University, UK); DR Antonio Cartelli (University of Cassino,, Italy); Rommert Casimir (Tilburg University , The Netherlands ); Dr Ivana Cechova (University of Defence, Czech Republic); Maria Celentano (University of Lecce, Italy); Dr Valentina Chappell (Friends University, USA KS,); Athina Chatzigavriil (LSE, UK); Dr Phaik Kin Cheah (University Tunku Abdul Rahman, Malaysia); Dr Esyin Chew (University of Glamorgan, UK); Dr Satyadhyan Chickerur (B V Bhoomaraddi College of Engineering and Technology, Hubli, India.); Dr Lucian Ciolan (University of Bucharest, Romania); Dr Melanie Ciussi (SKEMA Business School, Sophia Antipolis, France); Dr Barbara Class (University of Geneva, Switzerland); Prof. Dr. Jürgen Cleve (Wismar University, Germany,); Dr Lynn Clouder (Coventry University, UK); David Comiskey (University of Ulster, Northern Ireland,); Professor Thomas Connolly (University of West of Scotland, UK); Prof Grainne Conole (University of Leicester, UK, www.e4innovation.com); Sarah Cornelius (University of Aberdeen, UK); Dr Marija Cubric (University of Hertfordshire, UK,); Ken Currie (Edinburgh University, UK); Dr Valentina Dagiene (Vilnius University, Lithuania); Mark De Groot (Leeds Metropolitian University, UK); Antonio De Nicola (ENEA, Italy, Italy); Prof/Dr Carmen De Pablos Heredero (Rey juan Carlos University, Spain); Dr. Rajiv Dharaskar (GH Raisoni College of Engineering, Nagpur, India); Prof Vicenzo Di Lecce (Politecnico di Bari, Italy); Martina Doolan (University of Hertfordshire, UK); Dr Yanqing Duan (University of Luton, UK); Dr Jane Eberle (Emporia State University, USA); Dr Colin Egan (University of Hertfordshire, Hatfield, UK); Dr. Ibrahim M. Elbeltagi (Plymouth University, UK); Dr Bulent Gursel Emiroglu (Eskisehir Yolu 20.km. Baglica Mevkii, Turkey); Foluke Eze (Federal College Of Education(Technical), Nigeria); Prof Liz Falconer (University of the West of England Bristol, UK,); Prof Gert Faustmann (Berlin School of Economics and Law, Germany, www.hwr-berlin.de); Prof Corona Felice (Faculty of Medicine and Surgery, University of Salerno, Italy); Rachel Fitzgerald (University of Northampton, UK,); Prof. Andrea Floros (Ionian University, Greece); Duncan Folley (Leeds Metropolitian University, UK); Dr Gabriele Frankl (Alpen-Adria-Universität vii

Klagenfurt, Kärnten,); Dan-Adrian German (Indiana University School of Informatics and Computing, USA,); Prof Itana Gimenes (Universidade Estadual de Maringá, Brazil); Dr. Katie Goeman (University of Leuven, Belgium (KU Leuven)., Belgium); Jetse Goris (University of Groningen, The Netherlands); DR Susan Greener (University of Brighton, UK); Dr. Michael Grosch (Karlsruhe Institute of Technology, Germany); David Guralnick (Columbia University and Kaleidoscope Learning, New York, USA); Dr Richard Hall (De Monfort University, Leicester, UK); Patricia Harvey (Greenwich University, London, UK); Thanos Hatziapostolou (International faculty of the university of sheffield, Greece); Dr Tali Heiman (The Open University, Israel); Alan Hilliard (University of Hertfordshire, Hatfield, UK); Uwe Hoppe (Bildungswerk der Sächsischen Wirtschaft gGmbH, Germany); Dr Md. Fokhray Hossain (Daffodil International University (DIU), Bangladesh); Rob Howe (The University of Northampton, UK,); Stefan Hrastinski (KTH Royal Institute of Technology, Sweden); Dr Maggie Hutchings (Bournemouth University, England, UK); Dr. Eun Hwang (Indiana University of Pennsylvania, USA); Balde Idiatou (Noble Group Organised Solutions, Guinea); Dr. Olimpius Istrate (University of Bucharest, Romania,); Dr Antonin Jancarik (Faculty of education, Charles University, Czech Republic); Amor Jebali (University of Manouba, Tunisia); Dr Amanda Jefferies (University of Hertfordshire, Hatfield, UK); Runa Jesmin (Global Heart Forum, UK); Dr John Jessel (Goldsmiths, University of London, United Kingdom,); Aidan Johnston (University of Strathclyde, UK); Geraldine Jones (University of Bath, UK); Paul Jones (University of Plymouth, UK); Dr Jowati Juhary (National Defence University of Malaysia, Malaysia); Dr Michail Kalogiannakis (University of Crete, Faculty of Education, Crete); Clifton Kandler (University of Greenwich, UK); Catherine Kane (Trinity College Dublin, Ireland); Jana Kapounova (University of Ostrava, Czech Republic); Dr. Elisabeth Katzlinger (Johannes Kepler University, Austria); Dr Andrea Kelz (University of Applied Sciences Burgenland,Campus Pinkafeld, Austria); Kaido Kikkas (Estonian IT College + Tallinn University, Estonia,); John Knight (Bucks New University, UK,); Dr Jasna Kuljis (Brunel University, UK); Prof Sunaina Kumar (Indira Gandhi National Open University, New Delhi, India); Dr. Swapna Kumar (University of Florida, USA); Blair Kuntz (University of Toronto, Canada); Professor Eugenijus Kurilovas (Vilnius Gediminas technical university / institute of mathmatics and informatics of Vinius University, Lithuania); Eleni Kyza (Cyprus University of Technology, Lemesos, Cyprus); Dr Yacine Lafifi (LabSTIC Laboratory, Guelma University, Algeria); Dr Maria Lambrou (University of the Aegean Business School, Greece); Andy Lapham (Thames Valley University, UK); Dr Mona Laroussi (Institut National des Sciences Appliquées et de la Technologie, Tnis and Lille, Tunisia); Jake Leith (University of Brighton, United Kingdom,); Kate Lennon (Glasgow Caledonian University, UK); Mariana Lilley (University of Hertfordshire, UK); Dr Jorgen Lindh (Jonkoping International Business School, Sweden); Dr. Gi-Zen Liu (National Cheng Kung University, Taiwan); Dr Ying Liu (Cambridge University, UK); Dr. Kim Long (Wiley College, USA,); Jenny Lorimer (University of Hertfortshire, UK); Ana Loureiro (Politechnic Institute of Santarem - School of Education, Portugal); Prof Sam Lubbe (University of South Africa, South Africa); Dr Robert Lucas (Keylink Computers Ltd, Kenilworth, UK); Dr Nick Lund (Manchester Metropolitan University, UK); Prof Zdena Lustigova (Charles University in Prague, Czech Republic); Dr Martin Magdin (Constantine the Philosopher University in Nitra, Faculty of Natural Sciences, Slovakia); Adnan Mahmood (University of Jinan, China); Dr. Chittaranjan Mandal (Dept of Computer Sc & Engg, IIT Kharagpur , India); Augostino Marengo (University of Bari, Italy); Dr Lindsay Marshall (Newcastle University, United Kingdom,); Dr Maria J Martinez-Arguelles (Universitat Oberta de Catalunya, Spain); David Mathew (University of Bedfordshire, England,); Erika Mechlova (University of Ostrava, Czech Republic); Dr. Cherifa Mehadji (University of Strasbourg, France); Rosina Merry (the school of Education The Universityof Waikatio, New Zealand);Linda Joy Mesh (Universita degli Studi di Siena, Italy); Jaroslava Mikulecka (Universityof Hradec Kralove, Czech Republic);Dr PeterMikulecky(Universityof Hradec Kralove, Czech Republic);Mike Mimirinis (Middlesex University, London, UK); Julia Mingullon (Universitat oberta de catalunya, Spain); Dr Ali Moeini (University of Tehran, Iran); Dr Jonathan Moizer(PlymouthUniversity, UK);Johann Moller (University of SouthAfrica (UNISA), South Africa); Dr. Begona Montero-Fleta (Universitat Politecnica de Valencia,Spain); Prof Lina Morgado (Universidade Aberta, Portugal); Kate Mottram (Coventry University, UK,); Peter Mozelius (Stockholm University,Department of Computer and Systems Sciences, Sweden,); Prof RadouaneMrabet (ENSIA, Morocco); Dr Antoinette Muntjewerff (University of Amsterdam Faculty of Law,Netherlands); DrMinoru Nakayama (Tokyo Institute of Technology, Japan); Dr Michaela Nettekoven (WU Vienna University of Economics and Business, Austria); Dr PaulNewbury (University of Sussex, UK); Professor Julian Newman (GlasgowCaledonianUniversity,UK); Emanuela-Alisa Nica (Center for Ethics and Health Policy and ,PetreAndrei University from Iasi, Romania); Dr Chetsada Noknoi (Thaksin University, Songkhla, Thailand);Dr Abel Nyamapfene (University of Exeter, UK); Sinead O’Neill (Waterford Institute of Technology ,Ireland); Ass. Prof. Dr Birgit Oberer (Kadir Has University, Turkey); Dr MaruffAkinwale Oladejo (Federal College of Education (Special), Nigeria); DR Kamila Olsevicova (Univeristyof Hradec Kralove, Czech Republic); Laurence Olver (Brighton Business School, University of Brighton, UK); Rikke Orngreen (Aalborg University, Denmark); Dr Abdul Jalil Othman (Faculty of Education, University of Malaya ,Malaysia); Dr Kutluk Ozguven (International University of Sarajevo, Turkey); Dr Ecaterina Pacurar Giacomini (Louis Pasteur University, France); Dr. Alessandro Pagano (Universityof Bari, Italy); Vasileios Paliktzoglou (University of eastern Finland, Finland); Dr StefaniePanke (University of Ulm, Germany); George Papadopoulos (University of Cyprus, Cyprus); Prof Kyparisia Papanikolaou (School of Pedagogical and Technological Education, Greece); Dr.Iraklis Paraskakis (South East European Research Centre (SEERC)Research Centre of the Universityof Sheffiled, Thessaloniki,Greece); Dr AngieParker (Anthem College Online, USA); Paul Peachey (University of Glamorgan, Treforest, UK); Dr Arna Peretz (Ben Gurion Univeristy of the Negev, Israel); Dr. Carmen Perez-Sabater (Universitat Politecnica de Valencia, Spain); Christine Perry(Universityof the Westof England, Bristol,UK); Dr. DonatellaPersico (IstitutoTecnologie Didattiochje-Consiglio Nazionale Ricerche,Genova, Italy); Dr Christopher Perumalla (University of Toronto, Canada); Professor Pit Pichappan (Annamalai University, India); Prof Mário Pinto (Polytechnic Instituteof Porto, Portugal); Professor Selwyn Piramuthu (University of Florida, Gainesville, USA); Dr Michel Plaisent (University of Quebec inMontreal, Canada); LubomirPopelinsky (Masaryk University, viii

Czech Republic); Dr Maria Magdalena Popescu (Carol I National Defence University, Bucharest,Romania); Dr Francesca Pozzi (ITD-CNR, Italia);Andy Pulman (Bournemouth University, UK); Dr Muhammad Abdul Qadir (Mohammad Ali Jinnah University, Islamabad, Pakistan); Prof Ricardo Queirós (ESEIG/KMILT & CRACS/INESC, Portugal);Susannah Quinsee (City University,London, UK); Prof AliRaddaoui (University of Wyoming, Wyoming); Abdul Rafay (Asia Pacific University College of Technology & Innovation, Malaysia);Dr Liana Razmerita (Copenhagen Business School, Denmark);Hugo Ribeiro (University of Porto, Portugal); Dr Bart Rienties (University of Surrey, UK,);Dr Eleni Rossiou (University of Macedonia, Greece); Dr Florin Salajan (North Dakota State University, Canada); David Sammon (University College Cork, Ireland); Marie Sams (Coventry University, England,); Gustavo Santos (University of Porto, Portugal);Prof Vitor Santos (University of Trás-os-Montese AltoDouro (UTAD), Portugal,); Dr Venkat Sastry (Defence College of Management and Technology, Cranfield University, UK); Dr Guy Saward (University of Hertfordshire, UK); Brian Sayer (University of London,UK); Prof. Jeanne Schreurs (Hasselt University, Diepenbeek, belgium); Dr Jane Secker (London School of Economics,UK); Drfabio Serenell i (Università degli Studi Milano Bicocca, Italia,); Dr NimaShahidi (Islamic Azad University-Noorabad Mamasani Branch, Iran,); Zaffar Ahmed Shaikh (IBA Karachi, Pakistan); Angela Shapiro (Glasgow Caledonian University, UK); Dr Michael Shoukat (UMUC, USA); AileenSibbald (Napier University, Scotland,UK); Dr Petia Sice (University of Northumbria, Newcastle-upon-Tyne, UK); Prof Ali Simsek (Anadolu University, Turkey); Dr Gurmeet Singh (The Universityof The South Pacific, Suva , Fiji, Fiji); Professor Cees Th.Smit Sibinga (Academic institute for the international development of transfusion medicine, The Netherlands); Alisdair Smithies (Manchester Medical School, UK); Dr Keith Smyth (Napier University, Edinburgh, UK); Bent Soelberg (Copenhagen Business School, Denmark); Yeong-Tae Song (Towson University, Maryland, USA);DrMichael Sonntag (FIM, Johannes Kepler University,Linz, Austria);Dr Sonia Sousa(Tallinn University,Estonia,); Dr Rumen Stainov (University of Applied Sciences, Fulda, Germany); Dr. John Stav (Sor-Trondelag UniversityCollege, Norway);Iain Stewart (Glasgow Caledonian University, Scotland); Caroline Stockman (University of Leuven,Belgium,); Mag. Dr.Thomas Strasser (Vienna University of Education, Austria); Karen Strickland (Edinburgh Napier University, Scotland,); Dr Amanda Sykes (University of Glasgow,United Kingdom); DrRoxana Taddei (Université Clermont Ferrand 2,Montpellier, France); Yana Tainsh (University of Greenwich,, UK); Bénédicte Talon (Université du Littoral, France); Marian Theron (False Bay College, Tokai, South Africa); Dr. John Thompson (Buffalo State College, USA); Dr Claudine Toffolon (Universitédu Mans-IUT deLaval, France); Florica Tomos (South Wales University, Wales, UK); Dr Eulalia Torras-Virgili (OpenUniversity of Catalonia, Spain); Dr. Melih Turgut (Eskisehir Osmangazi University, Turkey); Christopher Turner (University of Winchester,UK);Karin Tweddell Levinsen (Aalborg University, Denmark); DrAimilia Tzanavar (University of Nicosia, Cyprus); Prof Huseyin Uzunboylu (Near East University, Cyprus); Dr LindaVan Ryneveld (Tshwane University of Technology, Pretoria, South Africa); Professor Carlos Vazde Carvalho (Porto Polytechnic, Portugal); Prof Andreas Veglis (Aristotle University of Thessaloniki, Greece); Dr Steven Verjans (Open Universiteit of The Netherlands,The Netherlands); Anne Villems (University of Tartu, Estonia); Bruno Warin (Université du Littoral, Calais, France); Fahad Waseem (University of Northumbria, Middlesbrough, UK); Garry Watkins (University of Central Lancashire,UK); Jaap Westerhijs (University of Groningen, Netherlands); Dr Anne Wheeler (Aston University, UK); Nicola Whitton (Manchester Metropolitan University, UK); Roy Williams (University of Portsmouth, UK); Dr Shirley Williams (University of Reading, UK); Dr Katherine Wimpenny (Coventry University, England,); Prof StanislawWrycza (University of Gdansk, Poland); Rowena Yeats (University of Birmingham, UK); Dr Panagiotis Zaharias (Open University of Cyprus, Greece); Dr/ProfQinglong Zhan (Tianjin Universityof Technology and Education, China); Mingming Zhou (Nanyang Technological University, Singapore); Chris Zielinski (External relations and Governing Bodies,World Health Organization, Geneva, Switzerland); Anna Zoakou (Ellinogermaniki Agogi, Greece)

Biographies Conference Chair Dr Mélanie Ciussi is Professor of Education & ICT at SKEMA Business School, France, and also responsible for SKEMA’s Innovative Teaching and Learning projects, as well as a a researcher at I3M (Information and Communication Science). Her PhD focused on networks and communities of practice in virtual learning environments. A related domain of expertise is Mobile Learning, where she won the Apple Research & Technology Support programme prize in 2011. Melanie was also project head for a 2-year serious games initiative sponsored by the French ministry of research. Mélanie was previously employed by French Riviera Chamber of Commerce where she was responsible for e-learning. She holds Masters Degrees in Marketing (1996) and Training & Multimedia (2002). Before moving into research, she worked for Marks and Spencer for 3 years as Assistant Personnel Manager across Scotland and Belgium.

Programme Chair Dr Marc Augier is the chair of the Organization and Information systems department at SKEMA Business School. He is also a Professor in Management ofInformation Systems in SKEMA Business School since 2001 and has a Doctorate (2005) in Information and Communication Science. His research focuses on the relationship between science, technology and society, centered on the implication of the usage of IT technology in pedagogy. Therefore he is interested in Digital documents and libraries, Online Communities, hypertext as a knowledge representation tool and Free Software. Before joining SKEMA he worked in IT and consulting companies like IBM and Accenture. He has a solid background in computer science with a Masters degree from CESTI (1985).

Keynote Speakers Dr Viktor Dörfler gained masters degrees in Mathematical Engineering, International Business Relations, Engineering Education and an MBA from Hungarian universities. He holds a PhD in Management Science from the Strathclyde University, Glasgow, UK. Before joining Strathclyde University, he worked as lecturer in managerial decision making, creative problem solving and information management at the Budapest University of Technology and Economics, Hungary. Simultaneously he was an independent software development consultant specializing in intelligent systems. Viktor’s research is focused on two interrelated areas: the first covers the modelling of personal knowledge and knowledge increase in an organizational context; the second covers knowledge-based expert systems, in particular the Doctus KBS (www.doctuskbs.com), and related intelligent applications such as intelligent corporate portals and e-learning systems. Viktor’s research into personal knowledge informs his software development; using Doctus to support decision takers, in turn, helps him advance his research about knowledge. Viktor’s research, software development and consultancy are synthesized in his teaching. In his most recent research Viktor modelled levels of personal knowledge, with particular focus on the highest level of knowledge, for which he conducted 20 in-depth research interviews, including 17 with Nobel Laureates. Professor Steven Warburton is the Head of Department of Technology Enhanced Learning at the University of Surrey and an Associate Research Fellow at King’s Learning Institute, King’s College London. He is also a Fellow at the Centre for Distance Education at the University of London International Programmes where he leads work within the research strategy group and chairs the annual Research and Innovation in Distance and Elearning Conference. He has worked on a range of national and European projects that have included: the development a methodology for abstracting design patterns through shared expert practice; explorations of teaching practice in virtual worlds; developing pattern languages in the domains of digital identity and social media tools. More recently he has been working on digital competences and digital fluency, mobile learning, digital publishing models, open educational resources and educational analytics.

Mini Track Chairs Dr Marc Augier is the chair of the Organization and Information systems department at SKEMA Business School. He is also a Professor in Management of Information Systems in SKEMA Business School since 2001 and has a Doctorate (2005) in Information and Communication Science. His research focuses on the relationship between science, technology and society, centered on the implication of the usage of IT technology in pedagogy. Therefore he is interested in Digital documents and libraries, Online Communities, hypertext as a knowledge representation tool and Free Software. Before joining SKEMA he worked in IT and consulting companies like IBM and Accenture. He has a solid background in computer science with a Masters degree from CESTI (1985).

Dr Jana Kapounova is an associate professor at the Department of ICT, University of Ostrava in the Czech Republic. She teaches Educational Technology, ICT in Education and eLearning. She studied ICT in Education and holds bachelor, masters and Ph.D degrees. Her research field is eLearning and the evaluation of its quality. With her Ph.D students she works on the problem of approaches to personalised learning in different situations (at school, LLL, extracurricular activities etc.). Dr Rikke Orngreen is Associate Professor (PhD) at the IT and Learning Design Lab in the Department of Philosophy and Learning, Aalborg University, Denmark. Her primary research interests are methods, tools and processes in the development, implementation and evaluation of IT supported learning and teaching processes. Of particular interest is the use of digital video conferencing and methods that support creative and reflective learning processes, as various co-production tools. Her research focuses on both the actual situation as it unfolds as well as on how to facilitate the learners, teachers’ competence development, as well as the organizational setup. Dr. Ali H. Raddaoui, Graduate of Indiana University of Bloomington, Fulbright Fellow, Associate Professor of Applied Linguistics and Arabic at the University of Wyoming in Laramie. Taught English, French and Arabic in UK, Tunisia, Saudi Arabia, the UAE, and the USA. Areas of interest and publication: Teaching English as a Foreign/second Language; Teaching Arabic as a Foreign Language; Web 2.0 and education 2.0; best practices in language teaching and learning; ICT and language evolution; intercultural locution; translation, and creative writing. Jorge Tiago Martins is a Lecturer in Organisational Informatics at the Information School, University of Sheffield, UK. He is a member of the Information Systems (IS) and Knowledge and Information Management (KIM) Research Groups. He is the author of circa 15 refereed articles published in books, academic conferences and academic journals. His research interests include educational informatics and the management and use of information technology in complex organisations, with particular emphasis on structures, cultures, work practices, behaviour, and change.

Biographies of Presenting Authors Sally Abey, since working with the University of Plymouth, has developed an interest in the use of mobile devices in the area of teaching and learning. She is registered as a PhD student to carry out action research into the placement area. Paulo Alves – Ph.D. in Technology and Information Systems, University of Minho, Portugal, and Master in Multimedia Technology from the University of Porto, Portugal. Is e-learning coordinator and professor at the Polytechnic Institute of Bragança. The research interests include: e-learning, web development and multimedia. Roni Aviram is Chair, Center for Futurism in Education, Ben-Gurion University, Israel. He is interested in the impact of ICT on education and society and its optimization in the light of Humanistic values, and in structuring theoretical and practical change processes in education. He has led R&D projects dedicated to designing virtual LLL environments for enhancing human development. Rosalina Babo is a Coordinator Professor at the School of Accounting and Administration of Porto, Polytechnic Institute of Porto, Portugal. She is head of the Information Systems Department and was a member of the university scientific board for 12 years (2000-2012). E-Learning is one of her main areas of research. Wendy Barber is the Director of the B.Ed. Program at the University Of Ontario Institute Of Technology in Oshawa, Canada. Her research interests lie in Health and Physical Education, and Creating Online Communities. Dr. Barber is a passionate advocate for teacher education, teaches Authentic Assessment and Adult Education, and Psychological Foundations in Digital Technology. Raymond Bell works as a senior lecturer in Mental Health at Coventry University. His clinical background includes clinical nurse specialist of community psychiatry, clinical manager of community mental health teams, early intervention and assertive outreach teams. Raymond has developed a Mental Health Wellbeing pack at Coventry University which has been integrated into the pastoral care of Facility of Health and Life Science student nurses. xi

Orlando Belo is an associate professor in the Department of Informatics of Minho University, Portugal. He is also a member of the ALGORITMI R&D Centre in the same university, working in Business Intelligence, with particular emphasis on data warehousing systems, OLAP, and data mining. His main research topics are related to data warehouse design, implementation and tuning, ETL services, and distributed multidimensional structures processing. Diana Blom teaches music at the University of Western Sydney where she is Associate Professor. A published composer and pianist, research focuses on university music performance (inter-arts collaboration, assessment, interpretation) and the artist as academic. The five authors are a grant team researching the roles of ePortfolio and the creative arts in four Australian universities. Farida Bouarab-Dahmani is a senior lecturer in computer science in the computer science department of Tizi Ouzou University, Algeria. She has a doctorate and HDR in knowledge representation and evaluation process for e-learning. Her research is largely related to computer science use in the education field such as: assessment, domain modeling, competency based approach, educational data mining, e-learning. Vladimír Bradáč is an assistant professor at the Department of informatics and computers, Faculty of Science, University of Ostrava, Czech Republic. He teaches the English language focused on informatics. He also studies a PhD programme aiming at promoting and enhancing e-learning environment for language education. Margaret Bruce has been enthusiastically involved in podiatric education for more than 25 years, mostly spent at the University of Plymouth. She has been engaged in the development, organisation and delivery of the curriculum and am focused on supporting learning in practice. Sylvia Buchanan holds an Honours degree in Fine Art from York University in Toronto and completed a Master of Education degree in Digital Technology at the University Of Ontario Institute of Technology in Oshawa. Soon after, she obtained a graphic design diploma at the Digital Arts & Technology Training Institute in Sydney. Martial Bugliolo, BA(Hons), is Design for Games Programme Leader and Extended Diploma in Interactive Media Course Leader at Plymouth College of Art. He is currently researching, investigating and developing areas such as blended learning and elearning to identifying future technologies and how to enhance them to suit curriculum planning and delivery to support student’s needs. Mie Buhl, Professor, at Aalborg University, Copenhagen. Her research revolves around media, ICT and visual culture, with a particular emphasis on university education, teacher training and primary school. In this field she explores innovative designs of educational settings, in particular video and telepresence. She is one of the co-founders of Danish research in Visual Culture in education.. Martin Cápay is Assistant Professor in the Department of Computer Science, Constantine the Philosopher University in Nitra, specializing mainly in the theory of teaching informatics subjects, programming, behavioral of students in e-environment, and constructivist method of teaching informatics. He participates in the projects aimed at the usage of new competences in teaching and dealing with learning/teaching in virtual environment using e- learning courses. Ivana Čechová, Ph.D graduated from the Faculty of Arts at Masaryk University with specializations in pedagogy, English and Russian language. She has worked as Head of Research and Deputy Head of the Language Department. Currently she works as a senior lecturer at the University of Defence. In 2010 she completed her Ph.D. degree. Barbara Class is pedagogical advisor in distance learning issues at the University of Geneva, Interpreting Department since 2004. Her research interests include using technology for pedagogical purposes, tutoring support, active and collaborative learning in blended settings. Jürgen Cleve is professor of computer science at the University of Applied Sciences in Wismar (Germany). His fields are artificial intelligence and data mining. He is the head of the e-learning centre at Wismar University. David Comiskey is a lecturer in Architectural Technology at the University of Ulster. He is passionate about the use of technology in education and has received awards for embedding the use of technology in his teaching and learning. He was recently awarded a Distinguished Teaching Fellowship from the University of Ulster. Armando Cortés is PhD candidate and researcher at the eLearn Center of the Open University of Catalonia. Prior to doctoral studies, he worked as an instructional designer at the University of Barcelona and the Online Business School; he has been

xii

teaching in online courses for 10 years. Armando’s research interests focus on understanding success factors in online teaching and learning in higher education. Cristina De Castro graduated in Mathematics and received a PhD in Electronics and Computer Science. She is a researcher at IEIIT-CNR, National Research Council of Italy, Bologna, Italy. Her research themes are Smart Navigation and e/m-learning methodologies and architectures. Faiza Derbel is Assistant Professor of English and Linguistics at the Faculté des Lettres, Arts et Humanités, University of Manouba, Tunisia. She teaches graduate and undergraduate courses in various areas of Applied Linguistics and is currently conducting research on technology and language pedagogy, teacher cognition, and collaborative intercultural communication. Jakob Diel is working as scientific assistant at the e-learning centre at the University of Applied Sciences in Wismar, Germany. In this context he is developing and organizing online courses and the practical implementation of video based eassessments. Foluke Eze is running a doctorate programme in the faculty of Education, University of Nigeria Nsukka. She has been a lecturer in Federal College of Education (Technical) Omoku, Rivers state, Nigeria since 2001. Previously, she had taught further and general mathematics at both junior and senior secondary school for 2years. Gert Faustmann studied Information Technology at the Technical University Berlin. From 1992 to 2001 he was a software developer (Siemens AG), researcher (Fraunhofer Institute for Software and Systems Engineering) and consultant ( debis later T-Systems). He is now professor in the division of business information systems at Berlin School of Economics and Law. Álvaro Figueira is a lecturer at Faculty of Sciences, University of Porto, and has been interested in e-learning, web-based learning, standards in education, and information mining. Lately, he has been serving as the FCUP’s coordinator in respect to e-learning. Prof. Figueira’s current research interests are in the area of learning analytics related with collaborative work. Olga Fragou is an Instructional Designer in Educational Content, Methodology and Technology Lab, at Hellenic Open University and is Head of the Learning Activities Team. During 2005-2008 she worked as a PhD researcher in the Educational Technology Laboratory, University of Athens. She also has working experience in programs of adult education and European Projects. Michelle French is a Lecturer in the Department of Physiology at the University of Toronto in Canada. She is interested in methods to foster student learning, critical thinking and communication skills. Michelle has received several teaching awards including an Excellence in Life Sciences Award: Undergraduate Teaching from the Faculty of Medicine at U of T. Elaine Garcia is Head of Blended Learning and Digital Development at Plymouth College of Art, Associate Lecturer at Plymouth University Business School and undertaking a part time PhD considering use of blogs in teaching and learning. Research interests include Web 2.0, Social Media, Blended and Technology Enhanced Learning, Blogs and Teaching and Learning. Jan Geryk is a PhD student of Computer Systems and Technologies at the Faculty of Informatics, Masaryk University Brno, Czech Republic. Jan has been employed as the university information system developer for more than 6 years. He is experienced in database systems and perl programming and his research interests include machine learning, data mining, especially educational data mining, and visual analytics. Nina Raphaela Godson is a senior lecturer/leader in Clinical Skills at Coventry University with a background of medical nursing. She has studied to Master’s level in Clinical Education. She has published several books including a chapter on E-learning in Nurse Education and has developed interactive E-learning resources on Infection Prevention and Cardiopulmonary Resuscitation for pre-registration student nurses. Sue Greener is Principal Lecturer at the University of Brighton Business School teaching Learning & Development, HRM, Business Context and Research Methods and has received a Teaching Excellence award from the University. She researches, advises and supervises in the fields of e-learning strategy, Technology Enhanced Learning and reflective learning. She is Editor of the academic journal Interactive Learning Environments, published by Routledge. Baylie Hart Clarida is a first year PhD student at Bournemouth University in the UK, studying strategies for digital inclusion focusing on diverse students. She is a qualified teacher and has a BA (Hons) degree in Education and a Masters degree in ICT and Education.

xiii

Veronika Havelková is a PhD Student at Charles University in Prague and lecturer of seminars ‘Use the GeoGebra in the Teaching of Mathematics’, ‘Mathematical Software‘, ‘Computer as an Assistant (not only) in the Teaching of Mathematics’. Dissertation topic is The Phenomena Influencing the Efficiency of the Use of Dynamic Mathematics. Michael A. Herzog is full professor for Business Management and IT at Magdeburg-Stendal University. His research is concerned with mobile systems, RFID-technology, knowledge management and e-learning. He founded several international operating IT-enterprises concerning media technology and software development. Michael holds a PhD in information systems and master's degree in computer science from Technische Universität Berlin. Jiri Hoffmann is currently in his second year as a PhD candidate at the Department of Information and Communication Technologies at the University of Ostrava, Czech Republic. His main research activity is focused on technological competencies and out of school activities. Jozef Hvorecky graduated PhD. in Computer Programming at the Academy of Sciences in Moscow. He is Professor of Computer and Information Sciences at School of Management in Bratislava, Slovakia. He is also Honorary Lecturer of the University of Liverpool. His research interests cover introductory programming courses, university management, and knowledge management. Gloria Otito Izu holds a Bachelor Degree in Biology Education, a Researcher with Colleges of Education Academic Staff Union, Nigeria. Her research focuses on e-learning and science teaching methodologies. Antonin Jancarik works as a senior lecturer in the Department of Mathematics and Mathematics Education, Faculty of Education, Charles University in Prague. He is working in the areas of algebra, use of ICT in mathematics education and game theory. Amanda Jefferies is a Reader in Technology Enhanced Learning at the University of Hertfordshire, where she leads the Technology Supported Learning Research group. Her interests relate to students’ experiences of using technology to support their learning and the development of supportive pedagogies. She was awarded a UK National Teaching Fellowship in 2011. Cristian Jimenez Romero has a Degree in computer science and data systematization, University Antonio Nariño, Colombia. Further BSc-Honours degree with emphasis in biological psychology and artificial intelligence from the Open University, UK. Cristian has worked as software engineer at Nokia-Siemens-Networks. He is currently doing PhD, at the Complexity science department, faculty of computing and mathematics, OU. Thesis “Intelligent assessment systems applied to massive open online education" Olga Kandinskaia is Assistant Professor of Finance and Director of Blended Learning at the CIIM (Cyprus International Institute of Management). She has 20 years of experience in teaching F2F courses in Cyprus, UK and Russia, and 3 years of experience with online/blended courses. Olga has an extensive record of publications, which include two books. Elisabeth Katzlinger is assistant professor at the Department of Data Processing in Social Sciences, Economics and Business, Johannes Kepler University Linz (JKU), Austria. She has degrees in business administration and business education. Her research focus is in business education and technology enhanced learning. Early childhood education and game-based learning are another research interests Carolyn King is the Understanding Dementia Massive Open Online Course co-ordinator, a lecturer in the School of Medicine at the University of Tasmania, and a Wicking Centre Research Associate. She has a PhD in Neuroscience and her research interests include the biology of dementia, therapeutic approaches in dementia, as well as the scholarship of learning. Tomoko Kojiri received the B.E., M.E., and Ph.D. degrees from Nagoya University, Japan, in 1998, 2000, and 2003, respectively. From 2003 to 2007, she was a research associate at Nagoya University. From 2007 to 2011, she was an assistant professor in Nagoya University. Since 2011, she has been an associate professor at Kansai University, Japan. Katerina Kostolanyova works in the Faculty of Education, Institute of Information and Communication Technologies, Ostrava in Czech Republic. She specializes in eLearning technology, especially adaptive eLearning. Her further professional growth focuses on students’ learning styles in the e-Learning environment. She is an author and co-author of almost forty professional articles and ten e-contents. Blair Kuntz has been the near and Middle Eastern Studies librarian at the University of Toronto library since 2003. Before this, he studied Arabic for Foreigners at the Balamand University in Lebanon and Birzeit University in Ramallah, Palestine. He has also studied Farsi and Turkish at the School of Continuing Studies of the University of Toronto. xiv

Matleena Laakso (M.Ed.) works as an Educational Developer at PAOK - ICT Network for Tampere Region Upper Secondary Education, in Finland. Her main competences are e-learning, social media in education, and mobile learning. She has previously worked as an expert in problem-based and cooperative learning. Twitter: @matleenalaakso Wolfram Laaser is currently a consultant for Worldwide Education, Austria. Formerly he was Academic Director at Fern University Hagen, Germany. He was consultant for the ELBEP EU Grundtvig Project 2008/2009 and on the International Panel, Higher Education Distance Learning in Portugal, 2009. His field of expertise is the development of multimedia courseware. Karin Tweddell Levinsen is Associate Professor at Aalborg University, Copenhagen. Her research circle around design for teaching and learning that involves IT in various forms and modalities, and emerging educational performance and practice. Therefore formal and informal learning contexts, digital literacy, teachers’ competences, class room practice and process management are intertwined with design for teaching and learning. Katerina Makri holds a Phd in the area of eLearning and teacher education. With a background in humanities and experience in teacher training and facilitation of teacher online communitites, she currently works as an associate at ASPETE (School of Technological and Pedagogical Education) and at the School of Philosophy of the University Of Athens, Greece. Brian McDonald is Lecturer and Programme Leader in Games Software Developmen t. He teaches Video Game Graphics at Glasgow Caledonian University and has organised the Glasgow site of the Global Game Jam. He participated in and hosted Jamming for Small Change, MolyJam, Culture Hack and Gathering the Voices Jams. His research interests include Computer Graphics and Student Engagement with Software Development. Peter Mikulecky, PhD, is full professor of Managerial Informatics at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. Head of the Department of Information Technologies, Director of Doctoral Study Programmes. The areas of his main scientific focus are: Intelligent Environments, Ambient Intelligence, Artificial Intelligence, Knowledge Management, Intelligent Systems, and their applications. Luísa Miranda - Ph.D. in Education in the area of Educational Technology and Master in Educational Technology, University of Minho, Portugal. Is Professor at the Polytechnic Institute of Bragança. The research interests include: educational technology and e-learning. Carlos Morais - Ph.D. in Education in the area of Teaching Methodology of Mathematics and Master in in Educational Technology, University of Minho, Portugal. Is researcher at ICCS-Research Centre for Child Studies, University of Minho, Portugal. Is Professor at the Polytechnic Institute of Bragança. The research interests include: educational technology, ICT applied to mathematics. Per Mouritzen Implementation and evaluation of IT supported learning with focus on video supported learning and videoconference for cross campus teaching. Research focus on: video and videoconference development in teaching at higher education, including cross campus. Peter Mozelius has been employed since 1999 as a teacher for the Stockholm University and the Royal Institute of Technology at the Department of Computer and Systems Sciences (DSV) in Kista, Sweden. He is currently working as an IT-Pedagogue and researcher. His research interests are in the fields of e-learning, game-based learning and ICT4D. Antoinette Muntjewerff is Assistant Professor General Legal Theory University of Amsterdam. Studied Educational Science (MSc) and Law (LL.M.). PhD research involved theoretical and empirical studies into legal case solving and structured design of instructional environments. Her research is in modelling legal knowledge and legal reasoning for developing electronic materials for learning the law. Lukáš Najbrt is a PhD student in the Department of ICT, University of Ostrava in Czech Republic. He works as a designer of educational audiovisual projects. Under the program of the Department of ICT, he is trying to extend the personalized learning field to the museum area, and has had several successful projects for museums and lifelong learning. Minoru Nakayama is a professor at Human System Science and CRADLE, Tokyo Institute of Technology, Japan. He graduated from Tokyo Gakugei University in 1983 and completed the M.E. program in 1985, and received a Dr. of Eng. degree from Tokyo Institute of Technology in 1989. His research concerns educational technology. Corinne Nell is a lecturer in the Department of Marketing and Retail Management at the University of South Arica (UNISA). She worked in the retail sector in South Africa for many years and developed an interest in retailing. Academic interests in-

clude retailing with a specific focus on Visual merchandising and store atmospherics, consumer behaviour, social media and consumers’ perception. Bernard Nkuyubwatsi is a PhD student at University of Leicester. He is researching OER, OEP and MOOCs for widening participation in Rwandan higher education. He was a faculty member at Kigali Health Institute and a member of the Task Force and Working Group on the University of Rwanda College of Open and Distance Learning. Anne-Mette Nortvig is a PhD student at Aalborg University Copenhagen with a project concerning the role of e-learning in the development of professional identity in professional bachelor programmes with a case from physiotherapy e-learning program. She is a member of the ResearchLab: IT, Learning and Design in Copenhagen. Jarmila Novotná is Professor at Charles University in Prague, Faculty of Education, Czech Republic; Her main fields of interest are Didactical conditions of transformation of students’ models of activities when grasping knowledge and skills; and Transfer of research results into practice. Smart Odunayo Olugbeko holds a Master Degree in Language Education, a Senior Lecturer at Adeyemi College of Education, Ondo, Nigeria, where he teaches curriculum and language methodology courses Ramona Georgiana Oros – PhD degree in engineering and master in international business administration, working as junior researcher at Carinthia University of Applied Sciences in the field of remote technologies and online labs. Active part in several international projects like EICL, E-pragmatic, eScience, IC-op regarding e-learning and development of online laboratories. Aisha Othman is currently a PhD student at the University of Huddersfield, UK. She has graduated the MSc course in Information System Management at the University of Huddersfield and was awarded BSc degree from University of Omer ALmukhtar, Libya. Her main research interests are on adaptive e-learning, simulations, virtual environments, asynchronous interaction, multimedia, online education. Kyparisia Papanikolaou is Assistant professor at the Department of Education, School of Pedagogical and Technological Education (ASPETE), since 2008. Her primary research interests focus on the design of web-based adaptive learning environments (INSPIRE, MyProject), web-based education and blended learning, computer science education and teacher professional development focusing on Technology Enhanced Learning. Maria Magdalena Popescu is an associate professor at Carol I National Defence University in Bucharest, Romania. With an ESL major, an MA in British Cultural Studies, and a PhD in Humanities, she is one of the military English blended learning initiators in the Romanian military. She has participated in two European funded projects-GALA.NoE –game and learning alliance and GEL-game enhanced learning. Claire Raistrick is a Senior Teaching Fellow at University of Warwick and an educational researcher in the Department of Educational Research at Lancaster University where she is a doctoral candidate (PhD in e-research in TEL). She is Principal Investigator researching educators’ self-evaluative practices when making technology enhanced learning innovations. Patient Rambe (PhD.) is a Postdoctoral Research Fellow in the Department of Computer Science and Informatics and a former Assistant Director of International Academic Projects at the University of the Free State, South Africa. His research interest is the innovative pedagogical use of social media and appropriation of emerging Web-based technologies in resourceconstrained academic environments. David Reymond Is Associate Professor at the University of Toulon. He works in the informetrics discipline, and he is specialized in webometrics. From 2009 to 2013 he was attached to the Mission Numérique pour l'Enseignement Supérieur (MINES) to assist the Ministry in the construction of a tool aimed to provide indicators of digital services usages. Gelareh Roushan-Easton is the Associate Dean for Education in the Business School and theme leader for Technology Enhanced Learning (TEL) in Bournemouth University’s Centre for Excellence in Learning. She is an avid enthusiast of TEL and believes emerging technologies offer innovative approaches in engaging students in learning. Danguole Rutkauskiene is Associate Professor within the Department of Multimedia Engineering at Informatics faculty at Kaunas University of Technology, President of National Association of Distance Education. Member of EADTU Management Committee, researcher in Advance Learning technologies, e-methodology, coordinator of great number of scientific projects; author and co-author of 26 books and 153 scientific publications.

xvi

Michele Salvagno is a PhD student in psychology at Bournemouth University (UK). He has a Degree in Psychology (University of Padua, Italy) and a Degree in Psychotherapy. He has worked for years as a teacher and tutor in e-learning projects. His research interests include psychological issues and practices to foster well-being in online learning contexts. Daniyar Sapargaliyev is deputy director of the Center for research and development at International Academy of Business in Almaty. He received his PhD from Eurasian National University. His research interests include mobile learning and using mobiles in education. He has written articles in refereed books, journals and conference papers. Emilie Šeptáková has been teaching the theory of databases, information systems, and web programming at university and has participated in several national research projects in the area. She is currently working in the introduction of semantic network of terms in adaptive e-learning system. Angela Shapiro, Senior Lecturer adopts academic literacies pedagogy, working with students from the School of Engineering and Built Environment, Glasgow Caledonian University. She is a founding member of the Gathering the Voices Association. Her research interests are evaluating the effectiveness of online learning in supporting students and eLearning approaches in teaching about the Holocaust. Ivana Simonova, PhD, has been at the Faculty of informatics and Management, University of Hradec Kralove, CR, since 1997. Research focus on ICT-supported process of instruction, distance education. Latest research projects: Evaluation of modern technologies contributing towards forming and development university students´ competences; A flexible model of the ICT supported educational process reflecting individual learning styles Florica Tomos - BSc (Econ), Diploma in Management & Semiotics, PGC (MA) Management & PD, PGD Accountancy & Costs, PGCE /MSc. Educ. (50%), PhD Student – HP Lecturer – Business Research, South Wales University, Wales, the United Kingdom. Nazime Tuncay has a degree in Mathematics and Computer Education, MSC in Applied Mathematics and Computer Science and PhD in Computer Education and Instructional Technology Department in Near East University in North Cyprus. Her research interests include e-education, u-education, virtual education, vocational education, game-based education, special education, web tools and distance education. Karel Vaculík is a PhD student of Informatics at the Faculty of Informatics Masaryk University Brno, Czech Republic. His research interests include graph mining and educational data mining. He is a member of Knowledge Discovery Lab FI MU. Stephen Wilkinson is a Principal Lecturer and Teacher Fellow at Leeds Met University; he is the course leader for the Masters in Advanced Engineering management. He has a Masters in Blended and online Education (BOE) and has co-authored 2 books in the field of Manufacturing Technology. His research has covered many areas from augmented reality in surgery to automation and control of manufacturing systems. Eleanor Vernon Wilson is an Associate Professor of Curriculum, Instruction and Special Education in the Curry School of Education at the University of Virginia. Her primary teaching and research activities focus on preparing elementary pre-service candidates for classrooms, both in the US and internationally. She currently directs a study abroad program for pre-service students in the UK. Hitomi Yukawa is a member of staff of the e-learning section of the Medical Education Promotion Center, Tokyo Medical University, Japan. Ibrahim Zincir is a lecturer in the Department of Computer Engineering at Yasar University in Izmir, Turkey. His main research and teaching areas are data mining, mobile network security and web programming. Dr Zincir holds BSc from Middle East Technical University (METU), Turkey, and MSc and PhD from University of Plymouth, UK.

xvii

Learning and Instruction in the Digital Age Antoinette Muntjewerff University of Amsterdam, Amsterdam, The Netherlands muntjewerff@uva.nl Abstract: To become a professional a person has to acquire knowledge and skills. The transfer of the specific knowledge and skills is organized and accredited within an institution as a school or a university. How people learn and what institutions can do to enhance learning is the major focus of research in the field of learning and instruction. The design of processes and resources for facilitating learning is the object of research of instructional technology. Working in the field of instructional technology for 25 years and having designed three major applications for learning legal tasks as solving legal cases (legal assessment), structuring and analyzing decisions by judges and selecting and stating facts, we now take the opportunity to describe what we think is a major issue in learning and instruction in the Digital Age, an issue that should be the main focus in developing instructional materials. We state that in designing instructional materials it is important to work in a principled and structured way. This may sound as a truism, however, we think it cannot be emphasized enough that using an explicit methodology for design can contribute to theory formation in the field of learning and in the field of instruction, it may also support theory formation in the domain at stake, in our case law, and it may result in materials that are properly evaluated and as such improve instruction and enhance learning. In this paper we describe a methodology for the design of resources for effective learning in the Digital Age. We illustrate the methodology by describing the design of our coaching system for learning legal case solving. Keywords: learning, instruction, instructional design, instructional design methodology, instructional technology, legal education

1. Introduction To become a professional a person has to acquire certain knowledge and skills (Ericsson et. al. 2006). One needs to learn the ‘State of the Art’ of the knowledge (concepts, theories, standard problems and their solutions) and the ways in which new knowledge is acquired in the field (methods). The transfer of knowledge and skills is organized and accredited within institutions as schools and universities. So, for example, law students go to university to learn to become a legal professional, and the universities organize that this learning takes place. How people learn and what institutions can do to enhance learning is the major focus of research in the field of learning and instruction. The design of processes and resources for facilitating learning is the object of research of instructional technology or educational technology. Instructional technology can be described as the theory and practice of design, development, utilization, management, and evaluation of processes and resources for facilitating learning and improving performance (Randy Garrison and Anderson 2003). There are many theories on learning and many theories on instruction (see, for instance, http://tip.psychology.org/theories.html for brief summaries of 50 major theories of learning and instruction, see also, for instance, Leonard 2002). These theories differ on many issues. However, one of the main shared statements made is that the major goal of instruction is to enhance efficient and effective learning. Merrill (2007) being in the field of instructional technology for forty years and founding father of computer assisted instruction, states that the first principles of instruction are and remain: activation, demonstration, application and integration. A major meta‐analysis of instructional research outcomes by Hattie (2008, 2012) shows that one of the major factors in effective learning is related to feedback. Keeping in mind this in fact simple starting point that instruction is designed to facilitate learning and confronted with the variety of learning theories and theories on instruction we think it is important that instructional materials are designed in a principled and structured way to be able to support both theory formation and the evaluation of materials to be able to select those materials that really enhance learning. Working in the field of instructional technology for 25 years and having designed three major applications for learning legal tasks as solving legal cases (PROSA see Muntjewerff, 2000, Muntjewerff and Breuker, 2001), structuring and analyzing decisions by judges (CASE see Muntjewerff, 2011) and selecting and stating facts (e‐See Muntjewerff and DeTombe, 2004), we now take the opportunity to describe the issue that should be the main focus in developing instructional materials. In designing instructional materials it is important to work in a principled and structured way. We think it cannot be emphasized enough that using an explicit methodology for design can contribute to theory formation in the field of learning and instruction; it may also support theory formation in the domain at stake, in our case law, and may result in materials that are properly evaluated and as such improve instruction and enhance learning

323

Antoinette Muntjewerff We describe a methodology for the design of resources for effective learning in the Digital Age. We will illustrate the methodology on the basis of the design of our coaching system for learning to solve cases PROSA. PROSA has been designed using our principled and structured design methodology. We will only use PROSA here for illustrating the methodology. The focus of our paper is our statement that to design materials for instruction that enhance learning and to contribute to theory formation, it is required to work in a principled and structured way, that is to use an explicit methodology.

2. Learning, instruction and technology There are many theories on learning and many theories on instruction and these theories differ on many issues. However, one of the main shared statements made is that the major goal of instruction is to enhance efficient and effective learning. “facilitation of learning is the only proper end of teaching” (Ausubel, 1967). “deliberate manipulation of learning processes by some external agency for the purpose of enhancing learning outcomes” (Ausubel, 1967) The design of processes and resources for facilitating learning is the object of research of instructional technology. Instructional technology can be described as the theory and practice of design, development, utilization, management, and evaluation of processes and resources for facilitating learning and improving performance (Randy Garrison and Anderson 2003). We state that the objective of instructional technology is to facilitate effective and efficient learning and that the means to reach this objective is by instruction (teaching). However, this is easier said than done. If we have a closer look at the three constituent elements being learning, instruction and technology we might say that each of these elements involves many issues and is a source of difficulties and problems. If we look at the constituent ‘learning’ the main problem here is that there are many theories on learning. See, for example, http://tip.psychology.org/theories.html and http://www.cloudnet.com/~edrbsass/edlea.htm which list and describe many different learning theories. A basic inventory of learning theories shows the following approaches (see Muntjewerff, 2000):

behaviorism

cognitive approach

information processing approach

symbol manipulation theory

connectionism

situated cognition

The main question in the field of learning still remains “how do we learn?”. If we look at the constituent ‘instruction’ we see the same as with learning. There are many theories on instruction. See for example http://www.usask.ca/education/coursework/802papers/mergel/brenda.htm and http://www.edtech.vt.edu/edtech/id/models/ which list and describe many different instructional theories. However, matters are even more complicated here because of the interrelation with learning. Instruction is dependent on the learning theory that is adapted. To make matters even more complicated there is also a relation between the instructional theory chosen and the instructional design requirements that have to be met. There are different instructional aspects (cognitive, affective, organizational) and many different instructional variables (actors, content, presentation) that have to be taken into account when designing instruction for effective and efficient learning. If we look at the constituent ’technology’ we differentiate between general technology applied to learning and instruction and technology specifically developed for learning and instruction. General technology such as: (streaming) video/film/photo, sound/light, print (books), software (office, programming, e‐mail, browsers, games), hardware (computers, mobile phones, (intelligent) whiteboards, beamers, overhead, video/sound recorders and players), semantic web, wiki.

324

Antoinette Muntjewerff Specific technology such as: courseware (computer assisted instruction, intelligent tutoring systems), electronic learning environments, educational modeling languages, course management systems, learning objects and meta data, e‐portfolio’s, smart e‐boards. The point we want to make is that in designing instruction for effective and efficient learning many decisions have to be made on learning, instruction and technology and their interrelations. How can we prevent not to get lost? How can we contribute to theory construction and testing of learning theories and instructional theories? How can we evaluate instruction and instructional materials to be able to conclude that the objective “facilitate effective and efficient learning” has been achieved? In our opinion an answer to these questions might be by way of a principled and structured design approach. In the next paragraph we describe our methodology for the design of resources for effective learning in the Digital Age.

3. A methodology for the design of resources for effective learning In designing instruction many decisions have to be made. These design decisions involve topics as the learning goal, how to reach that goal, what the students have to do, how things are explained when things go wrong, how to present the subject matter and the problem situations, how to keep track of what the student is doing (monitoring), how to bridge the gap from the analysis of the mistake to the instruction (the reaction), how to react to what the student is doing, how to communicate with the student etc.. Although many authors stress the importance of designing instruction on the basis of a theory on learning and instruction (see, for example, Gagné, 1965; Ausubel, 1968; Merrill, 1983; Gagné, Briggs and Wager, 1992), (legal) educational practice often lacks this theoretical foundation. The design resulting from such an unfounded approach is based on ad hoc and intuitive decisions and therefore makes it impossible to account for mistakes and difficulties arising from realizing the design. The same is true for the design of computer assisted instruction or intelligent tutoring systems (see, for example, Reichgelt, Shadbolt, Paskiewicz, Wood and Wood, 1993). “Clearly, one of the most important design decisions in the implementation of an Intelligent Tutoring System is the choice of the teaching strategy according to which the system tutors. Unfortunately, most of the work in Intelligent Tutoring Systems has ignored the psychological literature on effective instruction. “(Reichgelt et al., 1993, p. 239). It may be even truer for the design of learning environments. Many of the so called learning environments do not enhance effective and efficient learning at all (see Muntjewerff and Leijen, 2005). Therefore we stress the importance of a principled and structured design approach. The principled approach sees to the need of theory development on learning and instruction, combined with development of theories of the domain to be learned. Besides that it is necessary to learn from earlier work in the field of learning and instruction. See for example Merrill (2007) who has been active in the field of computer assisted instruction for over 40 years. He states that as far as learning and instruction are concerned we are confronted with what he calls “the first principles of instruction” Merrill (2007) being in the field of instructional technology for forty years and founding father of computer assisted instruction, states that the first principles of instruction are and remain: activation, demonstration, application and integration. A major meta‐analysis of instructional research outcomes by Hattie (2008, 2012) shows that one of the major factors in effective learning is related to feedback. Principled and structured design indicates that all decisions that are made during the design process should be made explicit. As far as learning and instruction are concerned we should select a learning theory and stick to it. Select a matching instructional theory and stick to it. Select a matching instructional design approach and stick to it (see for example, Merrill (2007, 1983) instructional design model http://id2.usu.edu/id2/index.htm; Merriënboer, Clark and de Croock (2002); Muntjewerff 2009, 2003, 2000). In principled and structured design the principled design part sees to the requirement that design of resources should be based on theories of learning and instruction and on domain theories. Structured design involves the process of realizing the materials for effective and efficient learning by decomposing the design task into subtasks which are placed in a fixed order with input and output relations (see Figure 1).

325

Antoinette Muntjewerff

3.1 Principled design To make and justify decisions we argue that the instructional design decisions should be based on instructional theory, where a proper instructional theory, in turn, should be based on a theory of learning. Adopting global, but related, theories on learning and instruction may offer prescriptions for arranging practical instruction resulting in a coherent and consistent instructional model. Teaching should aim at facilitating learning. Although this may seem rather obvious, even a truism, it claims a close relation between a model of learning and an instructional model. A theory of learning as basis and guideline has relevance for the design of the instruction. To understand the learning processes enables the discovery of the most effective methods of manipulating these learning processes. Therefore, to be able to arrange practical instruction on the basis of relations between learning processes and instructional activities it is necessary to select a theoretical framework that describes these relations as explicitly as possible. However, learning theories differ with regard to their view on learning processes, and instructional theories differ with regard to prescriptions on arranging instruction. We therefore formulate the following criteria for selecting the theoretical basis. principled design structured design basic research ‐ learning theories ‐ domain theories analysis model construction ‐ domain model ‐ instructional model design

applied research

construction

material construction evaluation

evaluation

implementation

integration research curriculum classification ‐ ToolBox ‐ WorkBench

Figure 1: Principled and structured design In our opinion a theoretical basis should indicate: an explicit description of learning, an explicit description of the relation between learning and instruction, an explicit description of how to arrange instruction to enhance and support learning, an explicit description of the relation between motivational issues and learning, an explicit description of how to arrange conditions in the instruction in such a way that motivational issues are taken into account. Principled design involves three interrelated research streams: basic research, applied research and integration research (see Table 1). Basic research is concerned with developing well‐founded models of knowledge and reasoning to be learned, examining the difficulties with acquiring knowledge and skills and finding remedies to enhance effective and efficient learning of the knowledge and skills.

326

Antoinette Muntjewerff Table 1: Principled and structured design approach basic research applied research integration

model construction materials construction remedies instructional model evaluation classification selection ToolBox

Workbench

theoretical research empirical research

domain view knowledge engineering view

Applied research is concerned with constructing applications for learning. Where a structured design guides the process in such a way that difficulties and mistakes encountered during the design process may be accounted for. Integration research is concerned with listing existing materials using a classification and to make applications available for (re) use in what is referred to as a ToolBox for learning. We state that these materials should be planned, designed and evaluated in a well‐founded and structured way. 3.1.1 Basic research The aim of the basic research part is to (re) construct explicit models of knowledge and reasoning to be applied in materials for learning. These models are (re) constructed by way of both theoretical and empirical research. In the theoretical research component we explore, conceptualize and specify knowledge and reasoning to be able to (re) construct explicit models of knowledge and reasoning. In the empirical research component studies are carried out to acquire insight in the way practitioners and scientists handle knowledge and in the way they use knowledge given a specific task. Besides that studies are carried out to acquire insight in the way students handle knowledge and apply this knowledge in performing a task. The outcomes give indications about specific difficulties in acquiring and using knowledge. Within the theoretical research component two perspectives are taken: a domain perspective and a knowledge engineering perspective. The domain perspective is that different domain sources are examined to specify models of knowledge and reasoning. These sources are empirical research, educational practice and theoretical research within the domain at hand. The knowledge engineering perspective within Artificial Intelligence research aims at constructing models of knowledge and reasoning. As these models have to be executed by a computer these models require a high level of explicitness. Explicitness is exactly what is required in instruction. 3.1.2 Applied research In the applied research part the materials for efficiently and effectively learning are designed in a principled and structured way, which implies that:

the basic research results are used in arranging the materials

the models of knowledge and reasoning are used in the materials

on the basis of insight in the specific difficulties of students in learning remedies are constructed to be used in the design of the materials

instructional design decisions are made on the basis of a global theory on learning and instruction. In this way the design process will result in a coherent and consistent instructional model

materials are evaluated extensively (developmental testing and field testing).

327

Antoinette Muntjewerff 3.1.3 Integration research It is self‐evident to be acquainted with existing materials. However, it is necessary to come up with a classification scheme to be able to integrate these existing materials in a ToolBox. This classification is useful 'to divide and conquer' the complexity and to make clear distinctions between types of materials and ways of realizing them. This division makes it easy to see what materials are already available and what materials are still missing and need to be constructed to really cover all aspects of learning in a specific domain. The main idea is to have these different types of materials available in a ToolBox for learning. In the ToolBox materials for learning are made available to teachers and students. The materials in the ToolBox are materials that cover a wide range of knowledge and skills to be acquired by the student to become a skilled practitioner or scientist. The student and the teacher may select the proper tools for learning or teaching. To be able to select the proper tools we also need to define selection criteria. The proposed classification distinguishes between communication tools for learning, information tools for learning and instructional tools for learning (see Table 2). Communication tools are materials that help to structure, organize and support communication in accomplishing a certain task (for instance, an online legal clinic, negotiation) Information tools are materials that contain data needed in order to carry out a certain task (for instance, databases of statutes). Instructional tools are materials for the effective and efficient acquisition of knowledge and skills. Instructional tools are materials that instruct. With this we mean that the materials are intended to support the learning of a certain body of knowledge or a certain (set of) skills. We classify instructional tools in three different categories:

Knowledge acquisition tools are tools that support the learner in acquiring the meaning of concepts and the relations between concepts

Training tools are tools that use the acquired knowledge in performing a (problem solving) task.

Test tools are tools that present the learner with assignments to test her knowledge and performance.

Table 2: Classification of electronic materials for learning the law

materials for learning

communication tools information tools Instructional tools

knowledge acquisition tools/guiding systems training tools/coaching systems

test tools

3.2 Structured design Structured design involves a design process in which subtasks are made explicit and are carried out in a fixed order. The process is an iterative process and is in fact a standard procedure in all types of materials design, as for example software design. The main issue here is to make decisions explicit to be able to account for mistakes or difficulties. Structured design process

analysis

construct explicit model of the task

study students difficulties

study remedies

select an instructional model

design

requirements

functional specification

architecture

interface

328

Antoinette Muntjewerff

construction

evaluation

does the resource help learners to improve their performance

implementation

implement resource in curriculum for enhancing effective and efficient learning

There is a relation between principled and structured in such a way that research results from the principled design part are (should be) used in the design process for realizing the materials. In turn outcomes from the evaluation in the structured design process are (should be) used in the principled design part to refute or support theoretical assumptions (see Figure 1).

4. The methodology applied: designing coaching systems for learning legal tasks We illustrate our methodology by describing the design of our coaching system for learning legal case solving, the application PROSA. The purpose of this paper is to clarify and illustrate the methodology, not to describe the application for learning to solve legal cases in detail. See for details about the design of PROSA and the other applications CASE and e‐See Muntjewerff (2000, 2011), Muntjewerff and Breuker (20010, Muntjewerff and DeTombe (2004).

4.1 Legal task model Designing and implementing instruction for training legal case solving to improve students legal case solving performance requires both a task to perform and the facilitation of the learning process. The first issue requires that the legal case solving task must be carried out correctly. Therefore we examined both the task of legal case solving and the content and structure of the legal knowledge involved in solving a legal case (see Figure 2).

4.2 Difficulties in learning the legal task The most central and recurring task in the legal domain consist of solving legal cases, i.e. the assessment of a case against the law, i.e. the rules as contained in legal sources (statutes, treaties, precedent cases). Typically, in an assessment task an abstract, interpreted case is matched against a set of norms. Despite the importance of the task, this task is hardly ever trained in regular legal education worldwide. select

legal rules

legal facts

component

situation description

complex reality

- match - select - interpret

- decompose

interpretation methods

- select

- select - proof

reasoning step reasoning step reasoning step reasoning techniques conclusion

CASE

e-See

PROSA

Figure 2: Explicit model of the legal task Extensive empirical studies performed in the 80‐ies show that students have major difficulties in solving legal cases, but that training them in applying well structured and systematic methods did not improve their performance. Indeed, studies performed by ourselves confirmed these results. We also found that students have difficulties, but that they appeared not to be due to lack of working systematically. In fact, students

329

Antoinette Muntjewerff worked as systematic as experts, and did not differ much from experts who solved cases in areas of law that were not their specialty. We concluded that students have problems in managing the information and with finding their way in the legal sources they have to use to construct a legal solution. The structure of legal sources, in particular statutes, differs largely from normal texts. A statute consists of individual statements (articles) which have hardly any coherence except for some global topics, so that it is difficult to acquire a coherent conceptualization of the law. Therefore there is no simple mapping between a case at hand and the text of the law, one has to go back and forth in order to identify the applicable articles. In educational practice, teachers try to convey the major conceptualizations of a piece of law by explaining how they are justified, but this is insufficient for operational purposes. Therefore, practice is unavoidable and this requires feed‐back and other guidance that cannot be afforded in regular education: this is another reason why there is an increasing interest in systems that train legal problem solving and argumentation. In other words, we have concluded that the difficulties of students are rather due to a problem of comprehending the content of a statute than to lack of an effective method for applying it to a case. Our hypothesis is that a systematic and articulate problem solving method emerges with practice and is not an initial driver of the correct performance. A second source of difficulties is in the nature of the solution itself. A solution requires finding all applicable articles, but this is only a first step. The applicable articles may be related or not; if they are related additional reasoning, in particular when an applicable article is an exception to another applicable one, is required to construct the final conclusion(s). This latter subtask involves keeping track of the current state of the solution. Our studies showed that students easily lost earlier results of their matchings.

4.3 Materials construction There are some important advantages of computer assisted or electronic materials for enhancing effective and efficient learning: individualized instruction, individualized practice, immediate support and feedback, capacity of adapting to individual students’ performance, information management support, various representations of knowledge, visualization of knowledge and tasks. We realized the instruction for learning legal case solving as a training tool or, as they are referred to in the Artificial Intelligence (AI) and Education community, as a coaching system (see Figure 3). A coaching system consists of an environment in which the student is enabled to perform the task to be learned or trained.

domain knowledge

e n v i r o n m e n t

d i a g n o s e r

m o n i t o r

c o a c h

student model

student

Figure 3: Coaching system The coaching system monitors the activities and outcomes of the student and compares these with the required activities and outcomes. These systems therefore imply some normative view (as most teachers have). A deviation is viewed as an error or inefficiency. When the coaching system encounters a deviation it subsequently diagnoses what may have caused it. The following functional components are distinguished: an environment to enable the task to be learned or trained, a monitoring component to observe and interpret the student’s behavior while she is performing the task and to identify that there is a deviation, a diagnoser to identify the cause of the deviation, a coach to assist and instruct the student, a student model being a

330

Antoinette Muntjewerff repository where the information about the student is collected to built a model of the individual student (see, for instance, Sleeman and Brown, 1981). The model keeps track of the changes in behavior and registers what the student is doing and how she does it.

4.4 Select remedies In designing PROSA (PROblem Situations in Administrative law) as an instructional environment for learning to solve legal cases the aim was in the first place to install remedies for the two difficulties: the first one related to the content and the second one to the information management of the task. Our findings that difficulties in legal case solving are first of all caused by insufficient mastery of, or insight in, the subject matter and that methods emerge from problem solving rather than being the driving force have major consequences for the second issue the facilitation of learning. Instead of instructing an explicit method for legal case solving, we state that presenting the student guided access to the subject matter may be more beneficial (see Figure 4).

4.5 Instructional model In arranging practical instruction we took a top down approach to define our instructional model for learning to solve legal cases. A theory of learning is the basis for a theory on instruction, where a theory on instruction is the basis for an instructional model. Such a principled approach results in a coherent and consistent instructional model. Our instructional model reflects the learning outcome specified and distinguishes between instructional presentations and the supportive presentations. global theory of learning – conditions of learning (Gagne, 1965/1985) – learning outcomes – performance categories theory of instruction – Component Display Theory (Merrill, 1983) – content‐performance classification – presentation forms instructional model

Figure 4: PROSA an instructional environment for learning to solve legal cases

331

Antoinette Muntjewerff

4.6 Evaluation To test if PROSA is really as effective as claimed an experimental study was performed. The evaluation results show that only about twelve hours of working with PROSA is enough to double almost the performance on the tests (Muntjewerff and Breuker, 2001).

5. In conclusion Digital Age or Stone Age enhancing effective and efficient learning is the only proper end of instruction. Developing resources for effective and efficient learning should follow a principled and structured design approach. In this way design decisions are made explicit and can be accounted for. It is also important to work on theory development. Both theory development in the field of learning and instruction as theory development in the domain to be learned. It is therefore also of great importance that resources are evaluated properly. To enable meta‐analyses of resource design projects and to enable theory development on how we learn and how effective and efficient learning can be enhanced. Because in the end that’s what it comes to. And as long as ‘the Digital Age’ has not yet produced a learning device that can be implanted in or adjusted to the human body (see www.kevinwarwick.com/icyborg.htm) its instruction that has to do the trick.

References Ausubel, D.P. (1968) Educational Psychology. A cognitive view, Holt, Rinehart and Winston Inc. New York. Ericsson, K.A, Charness, N, Feltovic, P.J and Hoffman, R.R (eds.) (2006) The Cambridge Handbook of Expertise and Expert Performance, Cambridge University Press, New York. Gagné, R.M., Briggs, L.J. and Wager, W.W. (1992) Principles of Instructional Design, Harcourt Brace Jovanovich College Publishers, New York (fourth edition). Gagné, R.M. (1965) The Conditions of Learning, Holt, Rinehart and Winston, New York (1st edition). Hattie, J. (2012) Visible learning for teachers. Maximizing impact on learning, Routledge Ltd. Hattie, J. (2008) Visible learning. A synthesis of over 800 meta analyses relating to achievement, Taylor & Francis Ltd. Leonard, D. (2002) Learning Theories A to Z, Greenwood publishing group. Merriënboer van, J.G., Clark, R.E and deCroock, M.B.M. (2002) Blue Prints for Complex learning. The 4C/ID‐Model, Merrill, M.D. (2008) http://www.youtube.com/watch?v=i_TKaO2‐jXA Merrill, M. D. (2007) First Principles of Instruction. Presentation Florida State University April 6, 2007 "A Task‐Centered Instructional Strategy" http://mediasite.oddl.fsu.edu/mediasite/Viewer/?peid=5625589e‐436b‐4fd8‐9282‐53131a64fc71 Merrill, M.D. (1983) Component Display Theory. In Reigeluth, C.M. (Ed.). Instructional design theories and models: An overview of their current status, Lawrence Erlbaum Associates, Hillsdale, New Jersey. Merrill, M.D. (1987) “The New Component Design Theory: Instructional Design for Courseware Authoring”, Instructional Science, 16, pp. 19 ‐ 34. Muntjewerff, A.J. (2011). CASE Learning to Structure and Analyze a Legal Decision. ECEL 2011 University of Brighton, Brighton. Muntjewerff, A.J. (2009) “ICT in Legal Education”, German Law Journal (GLJ). Special Issue, Vol. 10, No. 06, pp. 359 – 406. ISSN: 2071‐8322. Muntjewerff, A.J. and Leijen, J.J. (2005) Unplugging Blackboard. Maharg, P. and Muntjewerff, A.J. (Eds). The Law Teacher Special Edition Key Issues in the Development and Use of ICT in Legal Education, Sweet & Maxwell, London. Muntjewerff, A.J. and DeTombe, D.J. (2004) A Generic Environment for Integrating Streaming Video in Legal Education e‐ See. Proceedings World Conference on Educational Multimedia, Hypermedia & Telecommunications. Charlottesville, VA: AACE, pp. 527 – 532. Muntjewerff, A.J. (2003) The HYPATIA project. Models of Legal Knowledge and Legal Reasoning in Computer‐assisted Instructional Materials for Learning the Law. Maharg, P. (Ed.). International Review of Law Computers & Technology, Volume 17, Number 1, March 2003, p. 109 – 115. ISSN 1360‐0869. Muntjewerff, A.J. and Breuker, J.A. (2001) Evaluating PROSA, a system to train solving legal cases. Johanna D. Moore, Carol Luckhardt Redfield & W. Lewis Johnson (eds.). Artificial Intelligence in Education. AI‐ED in the Wired and Wireless Future. IOS Press, pp. 278 ‐ 285. Muntjewerff, A.J. (2000) An Instructional Environment for Learning to Solve Legal Cases PROSA, Amsterdam. Randy Garrison, D. and Anderson, T. (2011/2003) E‐Learning in the 21st Century: A Framework for Research and Practice, Routledge. Reichgelt, H., Shadbolt, N., Paskiewicz, T., Wood, D. and Wood, H. (1993) EXPLAIN: On implementing more effective tutoring systems. Sloman, A. (Ed.) Prospects for Artificial Intellligence, IOS Press, Amsterdam. Sleeman, D. & Brown, J.S. (Eds.) (1982) Intelligent Tutoring Systems, Academic Press, London.

332

Effectiveness of Instructional Suggestions for Note‐Taking Skills in a Blended Learning Environment Minoru Nakayama1, Kouichi Mutsuura2 and Hiroh Yamamoto1 1 Human System Science / CRADLE, Tokyo Institute of Technology, Tokyo, Japan 2 Faculty of Economics and Graduate School of Engineering, Shinshu University, Matsumoto, Japan nakayama@cradle.titech.ac.jp Abstract: This paper presents the results of a case study which introduced good note‐taking models and the functions of note‐taking to participants in a blended learning course. Transformations in scores of note‐taking activity were observed using metrics which consisted of questionnaires. The survey was conducted at the beginning and the end of a blended learning university credit course. The valid number of participants was 55. Three factor scores for note‐taking skills were calculated from participants' responses using a factor loading matrix. In comparing the two factor scores, the factor score for note‐taking skills known as recognising note‐taking functions increased significantly due to the more thorough instructions. During the course, the factor scores of the second survey correlated with online test scores and the scores of note‐taking assessments, while some factor scores were unstable. The factors responsible for note‐taking skills increased significantly due to the lecturer’s instructions, and this also suggests that student's characteristics contributed to the improvements. Keywords: note taking, blended learning, note assessment, learning assessment, causal analysis

1. Introduction Note‐taking is a key learning activity, which has been studied in a variety of learning environments (Kiewra 1985, 1989; Kiewra et al. 1995; Kobayashi 2005), including online learning. Note‐taking requires cognitive effort because this activity is based on summarization and understanding of the context (Piolat et al, 2005). The relationship between some factors of note‐taking and learning performance at world‐wide university teaching has been established previously (Nye et al., 1984; Kiewra et al., 1995; Kobayashi, 2005). The results of several surveys which the authors have conducted illustrate that note‐taking behaviour contributes to performance in an online learning environment (Nakayama et al. 2010, 2011b). The effectiveness of student's characteristics and note‐taking skills are examined using various methods of statistical analyses (Nakayama et al. 2012a, 2012c). The results suggest that learning performance may improve when appropriate note‐taking skills are developed. To examine the practical skills of note‐takers, the notes of students who took better notes were extracted and analysed using lexical analysis (Nakayama et al. 2011a, 2012b), and factors of note‐taking skills were extracted. If students learn a procedure for good note taking and the functions of note‐taking skills, both their note‐ taking skills and their learning performance may improve. According to this hypothesis, the factor scores of note‐taking skills may increase when appropriate instruction has been provided. This hypothesis should be examined using an experimental survey. The following topics are addressed in this paper:

How does instruction regarding note‐taking improve factor scores for note‐taking skills?

How do student’s characteristics contribute to their note‐taking skills?

To answer the above mentioned questions, a survey was conducted after the lecturer taught students the key points of note‐taking.

2. Method 2.1 Courses The surveys were conducted during an Information System Network, Bachelor level course which was taught as a Blended learning course at a Japanese university. Face‐to‐face sessions with students were conducted

333

Minoru Nakayama, Kouichi Mutsuura and Hiroh Yamamoto every week, and the participants of this survey gathered in a distance video conferencing room to take part in a simultaneous lecture using a bi‐directional TV system. This course consisted of 15‐week face‐to‐face sessions (Nakayama et al. 2010). All participants were Bachelor students. They were able to take online tests outside of the lecture room every week, and they were also encouraged to take an on‐line test for each session, as a function of the learning management system (LMS). Also, a final exam was given at the end of the course. The total number of valid participants in the course was 55.

2.2 Note‐taking instructions All participants were required to present their notebooks to track the progress of their learning. In most sessions, the lecturer reviewed and assessed these notes every week. The contents of students' notes were evaluated using a 5‐point scale (0‐4), 4:Good, 3:Fair, 2:Poor, 1:Delayed, 0:Not presented (Nakayama et al. 2010). The scales for Good, Fair and Poor were the main ones analysed. The results of this assessment were never provided to students, in order to prevent them from influencing ordinary note‐taking behaviour, as was also the case with our previous surveys. In this survey, the lecturer taught all participants note‐taking skills, such as showing them examples of good notes, and providing them with hints about how to summarise useful information. Instructions were given to participants two times during the course, at the beginning and in the middle of the course. The survey was also conducted twice, at the beginning and at the end of the course, using the questionnaire which will be explained in the following section. Table 1: Question items and factor loading matrix

2.3 Characteristics of students Student's characteristics are concerned with their learning activity and performance. These indices have been surveyed previously during our prior studies, using existing constructs (Nakayama et al, 2007, 2008). These constructs are: Personality (Goldberg 1999; IPIP 2004), Information Literacy (Fujii 2007) and Learning Experience (Nakayama et al. 2007). Personality: To measure the personalities of students, a public domain item pool, the International Personality Item Pool (IPIP) inventory (IPIP 2004) was used. This five factor personality model was proposed by Goldberg

334

Minoru Nakayama, Kouichi Mutsuura and Hiroh Yamamoto (Goldberg 1999), and provides five component scores: `Èxtroversion'' (IPIP‐1), `Àgreeableness'' (IPIP‐2), ``Conscientiousness'' (IPIP‐3), ``Neuroticism'' (IPIP‐4) and `Òpenness to Experience'' (IPIP‐5). Information literacy: Information literacy inventories were defined and developed by Fujii (2007) to measure information literacy. The survey consists of 32 question items, and 8 factors emerge from these questions, as follows: interest and motivation, fundamental operational ability, information collecting ability, mathematical thinking (reasoning) ability, information control ability, applied operational ability, attitude, and knowledge and understanding. These 8 factors can be summarised as two secondary factors: operational skills (IL‐1), and attitudes toward information literacy (IL‐2) (Nakayama et al. 2008). The validity of applying this metric to university students has been confirmed (Fujii 2007). Learning experience: The authors measured students' online learning experience using a construct consisting of a 10‐item Likert‐type questionnaire. In previous studies, three factors were extracted, as follows: Factor 1 (LE‐F1): overall evaluation of the e‐learning experience, Factor 2 (LE‐F2): learning habits, and Factor 3 (LE‐F3): learning strategies (Nakayama et al. 2007).

2.4 Survey of note‐taking skills The note‐taking skills of students were measured using the questionnaire. These note‐taking skills may consist of note‐taking abilities, attitudes and techniques, and original inventories of these have been developed by the authors (Nakayama et al. 2011, 2012a). To extract the common note‐taking skill factors as unobserved latent variables, factor analysis was conducted using Promax rotation (Yanai and Ichikawa, 2007). The questionnaire consisted of 17 question items, and three factors were extracted, as with previous surveys (Nakayama et al. 2012c). Table 1 shows the question items and the factor loading matrix. The factors are F1: Recognising note taking functions, F2: Methodology of utilising notes, F3: Presentation of notes. In this paper, this questionnaire was surveyed two times, and the factor scores were calculated for each survey, and were then compared.

3. Results 3.1 Comparison of note‐taking assessments The notes students took were evaluated every week. The percentages of Good and Fair notes are summarised in Figure 1. The level of students who produced Poor notes was less than 10 percent; these are not shown in Figure 1. The left side shows the results of the survey once the lecturer had taught students note‐taking skills. The data is limited to 29 out of 55 students because the note assessments have conducted for on campus students. In most weeks, the percentages of Good and Fair notes are comparable. To compare these to the same survey without instructions, the survey results from a previous year are summarised on the right side of the figure using the same format. This course has been taught to students in the same department every year. The survey has also been conducted every year, but note taking instructions were not given prior to this year. In comparing the two figures, the tendencies are the same and look similar. But the percentages of Good note‐ takers shifts upward during the intermediate weeks. To examine the differences, a Chi‐square test was applied to the percentages between grades of notes (Good and Fair) and survey years (2011 and 2012), and the resulting Chi‐square is significant (Chi‐square=13.8, df=2, p<0.01). This result suggests that the percentages of note‐taking grades changed between the two surveys. The participants are different between the two surveys, as some factors produced differences. If these factors were ignored, the lecturer then encouraged participants to improve their note‐taking activity.

3.2 Factor scores The factor scores were calculated using the factor structure in Table 1. The factor scores for the first and the second surveys are summarised in Figure 2. The means of scores in the two surveys are at almost the same

335

Minoru Nakayama, Kouichi Mutsuura and Hiroh Yamamoto levels across the 3 factors. In the results of a t‐test, there is a significant difference in the first factor, therefore recognising note‐taking functions between the two surveys (t(53)=2.9, p<0.01). This suggests that students have learned the functions of note‐taking from the lecturer's instructions. On the other hand, there is no significant difference in the second and third factors, F2: Methodology of utilising notes, and F3: Presentation of notes.

Figure 1: Percentages of Note‐taking assessment across weeks in 2012 (N=29) and 2011 (N=40)

Figure 2: Mean factor scores between 1 and 2 surveys of note‐taking skills Table 2: Correlation coefficients of factor scores between the 1st and 2nd surveys

Though the figure shows the overall means, levels of individual development were also examined. Figure 2 shows the relationship between the two surveys. The horizontal axis shows the scores of the first survey, and the vertical axis shows the scores of the second survey. In Figure 2a, the left side shows the relationship of factor 1 between the surveys, and in Figure 2b, the right side shows the relationship of factor 2 between the surveys. Figure 2a indicates that both scores of the two surveys are high and that the deviations are relatively small. Most scores shift to higher levels in the second survey. In comparing Figures 2a and 2b, the deviations in scores in Figure 2b are larger than the ones in Figure 2b. To examine the relationship between the two surveys, correlational coefficients of the three factors were calculated. The coefficients are summarised in Table 2. All coefficients are significant, thus the correlational relationships are positive. The coefficient for Factor 1 is the highest, and the coefficient for Factor 2 is the lowest. The small coefficient means that there are some changes between the two surveys and the unstable responses of students.

336

Minoru Nakayama, Kouichi Mutsuura and Hiroh Yamamoto To conduct a detailed analysis, the differences in the two surveys is defined as the score of the second survey minus the score of the first survey. To examine the differences in factor scores, the correlational coefficients between the differences and the scores of the first and the second surveys were calculated again. The coefficients of these are summarised in Table 3. All coefficients between the differences and the scores of the first survey are negative, and all coefficients between the differences and scores of the second survey are positive. These results show that the high scores in the first survey decreased to the level of the scores of the second survey, while the high scores in the second survey increased from the level of the scores of the first survey. This phenomenon suggests that the lecturer’s instructions improve or influence every student, thus some students who are poor note‐takers are able to understand how to take better notes, and students who are good note‐takers may be confused about their note‐taking skills. In particular, the results of factor 2, Methodology of utilising notes, show the unstableness of understanding by students. Table 3: Correlation coefficients between factor scores and differences in the two surveys

Table 4: Correlation coefficients between factor scores and online test scores

Some of the factor scores of the second survey correlate with the mean scores of online tests (NT‐F1: r=0.28; NT‐F3: r=0.36) in Table 4, while at the beginning of the survey there were no significant relationships. The relationships between the scores of note‐taking assessments also show a significant correlation with the factor scores of the second survey. Therefore, the transformation of student's note‐taking skills may contribute to the improvement of their online test scores.

3.3 Causal analysis According to a comparison of the factor scores, the results show that the improvement in note‐taking skills is limited. However, overall skills should be examined, and compared between the two surveys. To examine the effectiveness of the lecturer's instructions, mean structural equation modelling as a regression analysis using causal analysis was introduced. As Table 1 shows the structure of the factors, the Note‐taking skills factor as the secondary factor can be defined using the three factors. The mean structure consisting of three factors is defined and illustrated as a path diagram in Figure 3. The left side shows the mean structure of the first survey, and the right side shows the mean structure of the second survey. The figure illustrates a regression relationship between the sources of note‐taking skills, since skill development may have affected the first and second surveys. It is hypothesised that the two sources consist of almost the same structure, and that this is a constraint for this model. Additionally, some scores of student's characteristics are introduced into the model as shown in Figure 3, because student's characteristics are concerned with the development of note‐taking skills. All parameters were estimated using structural equation modelling (SEM) software (AMOS). The results are shown in Figure 3. The model is significant (CFI=0.89, RMSEA=0.09). No significant coefficients are represented using parentheses. The standardised coefficient of the direct path of the sources from the first survey to the second survey is significant (0.66), and the mean of note‐taking skills increases during the course.

337

Minoru Nakayama, Kouichi Mutsuura and Hiroh Yamamoto Though the factors did not significantly affect the source of note‐taking skills in the second survey, most factors significantly correlated with the source of note‐taking skills in the first survey. When all factors of student's characteristics were removed, the index of the fitness of the model was not significant (CFI=0.75, RMSEA=0.17). This suggests that some factors of student’s characteristics can help the model fitness. Therefore, student's characteristics contributed significantly to their note‐taking skills. The index of the fitness of the model increased slightly when all factors of student's characteristics were introduced, while the coefficient of the direct path of sources from the first survey to the second survey is significant. The greatest improvements in note‐taking skills may depend on the factors of note‐taking skills, and key factors such as IPIP3 (Conscientiousness), IPIP4 (Neuroticism), IL‐f (information literacy: operational skills), IL‐s (information literacy: attitude) and LE‐F2 (learning habits) are displayed in Figure 2.

Figure 4: Causal relationships of factor scores between the 1 and 2 surveys

4. Conclusion To improve student's learning performance in an online learning environment, the lecturer taught all participants note‐taking skills, as note‐taking performance is a key activity in the course. The assessment results of notes taken by students using lecturer's instructions were compared with the results from a previous survey, and the rate of assessment changed between the two surveys. The possibility of improvement due to instruction given by the lecturer was confirmed. The effectiveness of instructions about note‐taking skills given during a blended course was measured using the proposed metrics. According to the survey results, the scores of note‐taking skills which recognise note‐ taking functions increased significantly due to the instructions given. The factor scores of the second survey correlated with online test scores and scores of note‐taking assessments, though some factor scores were unstable. When analysis using the mean structural equation modelling technique was employed, the results show that the impact of note‐taking skills improved significantly due to the instructions given by the lecturer. Also, it is suggested that student's characteristics contributed to this improvement.

338

Minoru Nakayama, Kouichi Mutsuura and Hiroh Yamamoto According to the results, more effective instructions about note‐taking that compliment the student's existing skill set, and which also carefully consider each student’s characteristics, are required. These will be subjects of our further study.

Acknowledgements This research was partially supported by the Japan Society for the Promotion of Science (JSPS), Grant‐in‐Aid for Scientific Research (B‐22300281: 2010‐2012).

References Fujii, Y. (2007) “Development of a Scale to Evaluate the Information Literacy Level of Young People ‐Comparison of Junior High School Students in Japan and Northern Europe”, Japan Journal of Educational Technology, Vol. 30(4), pp.387‐ 395 Goldberg L. R., “A Broad‐Bandwidth, Public Domain, Personality Inventory Measuring the Lower‐Level Facets of Several Five‐Factor Models". In I. Mervielde, I. Deary, F. De Fruyt, & F. Ostendorf (Eds.), Personality Psychology in Europe, Vol. 7, pp. 7‐28, Tilburg, The Netherlands: (Tilburg University Press, 1999). International Personality Item Pool, "A Scientific Collaboratory for the Development of Advanced Measures of Personality Traits and Other Individual Differences" (2001) Retrieved 27 October, 2004 from http://ipip.ori.org/. Kiewra, K. A. (1985) “Students' Note‐Taking Behaviors and the Efficacy of Providing the Instructor's Notes for Review”, Contemporary Educational Psychology, 10, pp.378‐386 Kiewra, K. A. (1989) “A Review of Note‐Taking: The Encoding‐Storage Paradigm and Beyond”, Educational Psychology Review, 1(2), pp.147‐172 Kiewra, K.A., Benton, S.L., Kim, S., Risch, N., and Christensen, M. (1995) “Effects of Note‐Taking Format and Study Technique on Recall and Relational Performance”, Contemporary Educational Psychology, 20, pp.172‐187 Kline, R.B. (2005) Principles and practice of structural equation modelling, Second Edition, The Guilford Press, New York Kobayashi, K. (2005) “What limits the encoding effect of note‐taking? A meta‐analytic examination”, Contemporary Educational Psychology, 30, pp.242‐262 Nakayama, M., Yamamoto, H., and Santiago, R. (2007) “The Impact of Learner Characteristics on Learning Performance in Hybrid Courses among Japanese Students”, The Electronic Journal of e‐Learning, 5(3), pp. 195‐206 Nakayama, M., Yamamoto, H., and Santiago, R. (2008) “Impact of Information Literacy and Learner Characteristics on Learning Behavior of Japanese Students in On line Courses”, International Journal of Case Method Research & Application, Vol. XX. No. 4. pp. 403‐415 Nakayama, M., Kanazawa, H., and Yamamoto, H. (2009) “Detecting Incomplete Learners in a Blended Learning Environment among Japanese University Students”, International Journal of Emerging Technology in Learning, Vol.4, Issue 1, pp47‐51 Nakayama, M., Mutsuura, K., and Yamamoto, H. (2010) “Effectiveness of Note Taking Activity in a Blended Learning Environment”, Proceedings of 9th European Conference on E‐Learning, pp.387‐393. Nakayama, M., Mutsuura, K., and Yamamoto, H. (2011a) “Evaluation of Student's Notes in a Blended Learning Course", International Journal of New Computer Architectures and their Applications, Vol.1, No.4, pp.1080‐1089 Nakayama, M., Mutsuura, K., and Yamamoto, H. (2011b) “Student’s Characteristics for Note Taking Activity in a Fully Online Course”, Proceedings of 10th European Conference on E‐Learning, pp.550‐557. Nakayama, M., Mutsuura, K., and Yamamoto, H. (2012a) “Causal Analysis of Student’s Characteristics of Note‐taking Activities and Learning Performance during a Fully Online Course”, The Third International Workshop on Interactive Environments and Emergent Technologies for eLearning (IEETel), Proceedings of IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communication, pp.1924‐1927 Nakayama, M., Mutsuura, K., and Yamamoto, H. (2012b) “Visualization Analysis of Student's Notes Taken in a Fully Online Learning Environment”, Proceedings of 16th International Conference on Information Visualisation, pp.434‐439 Nakayama, M., Mutsuura, K., and Yamamoto, H. (2012c) “Note‐taking skills and Student's characteristics in Online Courses”, Proceedings of 11th European Conference on E‐Learning, pp.388‐396. Nye, P.A., Crooks, T.J., Powley, M. and Tripp, G. (1984) “Student note‐taking related to university examination performance”, Higher Education 13, pp.85‐97 Pilot, A., Olive, T., Kellogg, R.T. (2005) “Cognitive Effort during Note Taking”, Applied Cognitive Psychology, 19, pp.291‐312 Toyoda, H. (2007) Kyobunsan kouzou bunseki (Structural equation Modelling) [Amos‐hen], Tokyo‐shoseki, Tokyo Yanai, H., Ichikawa, M. (2007) Factor Analysis, pp.257‐296, Rao and Shinharay ed. Handbook of Statistics, Vol.26, Elsevier, The Netherlands.

339

Evaluation of Massive Open Online Courses (MOOCs) From the Learner’s Perspective Bernard Nkuyubwatsi Institute of Learning Innovation, University of Leicester, Leicester, UK bn30@le.ac.uk Abstract: Massive Open Online Courses (MOOCs) is one of the most debated topics in the field of education. The polemic on MOOCs was sparked by the emergence of extension MOOCs (xMOOCs) in late 2011. Highly disputed issues about MOOCs range from their quality to the potential impact of the courses on higher education in both developed and developing countries. This paper discusses, from the learner’s perspective, the quality of MOOCs and their potential contribution to widening participation and improving quality in Rwandan higher education. To gain first‐hand experience as a learner, I enrolled in one cMOOC and three xMOOCs. I compared and contrasted the four MOOCs with my previous radio, online and face‐to‐face courses and learning with institutions across five continents. I conducted a cross‐case analysis of the four MOOCs and six categories under which my previous courses fall. Through this analysis, I identified the recurring patterns and organised them into five themes: openness, availability, diversity, delivery and interactivity. I argue that MOOCs are currently among the most open courses and can lead to meaningful learning. Face‐to‐face and other kinds of online courses provide more interaction with the tutor than MOOCs do. Face‐to‐face course also provide the campus experience with peers which is absent in online courses. However, MOOCs and other online courses are superior to face‐ to‐face courses in terms of flexible delivery and the 24 hour per day availability. Some MOOCs are also more diverse in terms of participants, activities and assessment. Despite various constraints, xMOOCs can mitigate financial difficulties and the shortage of higher education teachers in Rwanda. They can also trigger the empowerment among Rwandan students and learners. Both xMOOCs and cMOOCs can enable network creation and maintenance, multicultural educational experience and lifelong learning in that country. The paper closes with an argument that MOOCs can contribute to widening access and improving quality in higher education globally, and specifically in Rwanda. Keywords: MOOCs, higher education, online learning, face‐to‐face learning, radio learning, Rwanda

1. Introduction The exponential proliferation of Massive Open Online Courses (MOOCs) has ignited heated debates in the last two years. One of these controversial debates about MOOCs is on the issue of quality. On the one hand, MOOCs are hailed for their fit within a knowledge society (Levy and Schrire n.d.). According to Koller (2012) MOOCs provide each individual learner with opportunities to engage with the materials via formative assessments and the ability to personalise their learning environment. At Stanford University, students preferred taking Artificial Intelligence as a MOOC rather than face‐to‐face (Thrun 2012). On the other hand, MOOCs are criticized for their assessment methods that lack constructive feedback (Daniel 2012, Armstrong 2012) and for their low level of comprehensibility (Mazoue 2013, Edmundson 2012). The lack of critical, creative and original thinking (Bates 2012) and students’ low completion rates (Daniel 2012) have also been noted. Equally, the contribution of MOOCs to better access to higher education in developing countries is disputed (Thrun 2012, Koller 2012, Bates 2012 and Daniel 2012). Briefly, the dispute on MOOCs is ongoing. The acronym MOOC (Massive Open Online Course) was created in 2007 by Dave Cormier and Bryan Alexander to define Connectivism and Connective Knowledge, the open online course developed at the University of Manitoba by George Siemens and Stephen Downes (Daniel 2012). Anderson (2013) refines the definition of MOOC by exploring the four aspects of the acronym notably the massiveness, openness, the online nature and course features. He notes that MOOCs’ massiveness refers more to the scalability rather than a specific number of students. He also acknowledges the massiveness in terms of the number of students, but recommends a careful use of students’ numbers at the registration, course start, first assignment/quiz completion and course completion phases in the discussion of MOOCs’ completion rates. Anderson (2013) identifies six types of openness:

expansion of education beyond geographical barriers,

freedom of speech,

removal of restrictions on the learning content,

enrolment without prerequisite,

the freedom to determine the learning pace,

340

Bernard Nkuyubwatsi

the provision of a course free of charge.

Concerning the online aspect, he points out that MOOCs are not necessarily entirely online since some students in the same geographical location can meet face‐to‐face for mutual support and meet‐ups are encouraged in some courses. As for the course aspect, he highlights that MOOCs run for a specific time (p.3). MOOCs can be defined as online, non‐selective and tuition‐free courses that are usually addressed to a global audience of students. Among the MOOCs offered since the delivery of Connectivism and Connective Knowledge are Personal Learning Environments and Networks and Knowledge (PLENK) delivered in 2010, Online Learning for Today and Tomorrow (EduMOOC) provided in 2011, Education, Learning and Technology (Change11) offered in 2011/2012 and Learning Analytics (LAK12) taught in 2012 (Rodriguez 2012). All these MOOCs were categorized as connectivist MOOCs (cMOOCs) and are based on connectivist learning principles (Siemens, 2005). In late 2011, three MOOCs were offered at Stanford University. Artificial Intelligence, Machine Learning and Introduction to Databases ran concurrently (Rodriguez 2012). The first enrolled more students than the other two MOOCs, and more importantly, it became more famous, because of its instructor’s post‐course reaction. After co‐tutoring the course with Peter Norvig and graduating 20,000 students, Professor Sebastian Thrun resigned from Stanford. He launched Udacity (https://www.udacity.com/), a private MOOC provider, in January 2012. This move triggered a response from his colleagues, Daphne Koller and Andrew Ng who co‐ founded Coursera (https://www.coursera.org/) in April 2012. Unlike Udacity, Coursera focused on working in partnership with prestigious universities. This enabled Coursera to grow fast to over 4,000,000 students, 396 courses and 83 partnering institutions as it was published on the company’s MOOC platform homepage early July 2013. Five languages (English, Spanish, French, Chinese and Italian) and 25 fields of study were represented in Coursera courses. A few weeks after Coursera was started, Harvard and MIT launched edX (https://www.edx.org/). By this time, the MOOC competition was already intense. The three MOOCs taught at Stanford University mark the beginning of the extension MOOC (xMOOC) era. These MOOCs are essentially based on a didactic pedagogy. They are characterised by high student‐ multimedia content and student‐student interactions, but student‐teacher interaction is very low. As the name suggest, the goal of these MOOCs is to expand higher education beyond universities’ physical campuses. Many universities involved in the xMOOC movement hope to attract students to their paid courses through MOOCs. For this reason, xMOOCs resemble, in many respects, university courses offered for various qualifications. Many of them include lectures, reading materials, quizzes, assignments, exams and forum discussions. Students who successfully complete them are awarded certificate. However, these MOOCs are criticized for relying on either cognitive‐behaviourism (Rodriguez 2012) or behaviourism (Daniel 2012, Bates 2012). Briefly, the three MOOCs at Stanford University defined a new turn in online education. American universities monopolized the emerging xMOOC industry for only a few months before British ones reacted. In December 2012, the plan to launch FutureLearn (http://futurelearn.com/about/) was revealed. By May 2013, 21 British universities, the British Library, the British Council and the British Museum were partners in FutureLearn. In March 2013, Open2Study (https://www.open2study.com/), an Australia MOOC platform was launched. This platform was quickly followed by the OpenUpEd (http://www.openuped.eu/), a pan‐ European MOOC initiative launched in April 2013, and NovoEd (http://novoed.com/) was announced at approximately the same time at Stanford University. NovoEd claims that its unique contribution to the xMOOC industry is social interaction.

2. The controversial MOOC debate The debate on MOOCs was polarized after the surge of xMOOCs. The founders of xMOOC platforms claim that MOOCs are high quality courses provided free to learners all over the world (Koller 2012, Shaw 2012, FutureLearn 2013, Thrun 2012). Equally, Ripley (2012) commends opportunities opened by xMOOCs to learners who are unable to attend higher education in existing systems. The quality of xMOOCs’ content is so high that some universities have made agreements with Coursera to use its courses for their accredited programmes (Kolowich 2012). There has also been recognition of both strengths and shortcomings of xMOOCs (Mazoue 2013, Pérez‐Peňa 2012). Similarly, Mackness et al. (2010) found that some Connectivism and Connective Knowledge students were positive and satisfied while others were negative and frustrated (p. 267).

341

Bernard Nkuyubwatsi These authors also note that the overwhelming number of participants hinders meaningful connectedness and interactivity. There have also been more negative criticisms about xMOOCs. Daniel (2012) and Bates (2012) highlight that these course rely on the transmission of information, computer‐marked assignments and peer assessment. These courses are criticized for lack of constructive feedback (Daniel 2012, Armstrong 2012), their low level of comprehensibility (Mazoue 2013, Edmundson 2012), their lack of critical, creative and original thinking (Bates 2012) and their low completion rates (Daniel 2012). However, Anderson (2013) argues against criticisms on MOOC completion rates that are based on figures that include enrolees who are not interested in completing the course. Likewise, Fini (2009) contends that using the concepts of attrition and drop out for such students is inappropriate. Hence, the way MOOC completion rates are calculated needs to be revisited. Another source of dispute about MOOCs is their potential to improve access to higher education in developing countries. While Thrun (2012) and Koller (2012) are optimistic about this contribution, Bates (2012) and Daniel (2012) see such optimism as erroneous. They argue that widening participation in developing countries depends on both the provision of free courses and the accreditation of the learning achieved. In other words, open courses and open accreditation are equally important ingredients for widening participation in developing countries.

3. Methodology This present research was designed as a case study. To learn about MOOCs’ quality and their potential contribution to Rwandan higher education, I enrolled in four courses. The first course, Open Content Licensing for Educator (OCL4Ed), was a cMOOC offered by the Open Educational Resources Foundation (OERF) in partnership with the Commonwealth of Learning (COL) chair in OER at Otago Polytechnic, the UNESCO‐COL chair in OER at Athabasca University and the Creative Commons Aotearoa New Zealand. It ran 3‐14 December 2012 and attracted 328 participants from 60 countries. The other three courses were Coursera xMOOCs: Artificial Intelligence Planning (AIP) from University of Edinburgh, Internet History, Technology and Security (IHTS) from University of Michigan and Leading Strategic Innovation in Organizations (LSIO) from Vanderbilt University. OCL4Ed, IHTS and LSIO were selected based on my personal interest in the courses. As for AIP, my enrolment was triggered by my curiosity to learn how xMOOCs work and my inability to wait for five weeks before the start of the other courses I was interested in. There were various levels of course completion in each of the xMOOCs. The AIP could be completed at the awareness, foundation and performance levels which required achieving a grade of 37 percent, 60 percent and 75 percent respectively. The LSIO could be taken within the standard track awarded with a statement of accomplishment and the studio mastery track awarded with a statement of accomplishment with distinction. As for the IHTS, a statement of accomplishment and a statement of accomplishment with distinction were awarded to students who scored 90/120 and 105/120 respectively. These four MOOCs constitute four case units in my analysis. I also grouped into six categories the radio, face‐to‐face and online courses that I have taken. The first two categories are my British Broadcasting Corporation (BBC) and Voice of America (VOA) English learning and represent my experience of learning by radio. The next two categories represent my face‐to‐face learning experience: my undergraduate education with the National University of Rwanda (NUR) and my graduate education with Eastern Michigan University (EMU). As for the last two categories, my post graduate education (graduate in the American system) with the UK Open University (OU) and the eLearning Skills with the e‐ Academy in collaboration with the University of the Philippines Open University (UPOU) represent my online non‐MOOC learning experience. Adding these six categories to the four MOOCs gives me a total of ten cases for the current study. I undertook a cross‐case analysis. Firstly, I identified recurring patterns across the ten cases. Secondly, I organised those patterns into five themes: openness, availability, diversity, delivery and interactivity. Then, I established a chain of evidence related to the five themes across the ten cases. In discussing findings, I linked pieces of evidence to the related literature and various sections of this research paper. Finally, the discussion fed into the conclusion, which summarises major findings of my study.

4. Openness The four MOOCs’ openness may be considered in the light of Anderson’s (2013) six features of openness plus interoperability, the concept used to refer to compatibility across various types of technology such as computers, mobile phones and radio. These MOOCs were open in terms of entry requirements and everyone

342

Bernard Nkuyubwatsi could enrol in and take the courses free of charge. They were partially open in terms of geographical boundaries since they were only accessible in areas with Internet connectivity. Based on the Miniwatts Marketing Group’s (2012) statistics on Internet penetration, MOOCs in general can be open, at present, to only about 35 percent of the world population. Of the four MOOCs, only OCL4Ed was open in terms of licensing. Overall, VOA English courses (which are in the public domain) were the most open of my ten cases. In terms of geographical location, learning pace and interoperability, the four MOOCs’ openness is the same as that of other online courses. However, the non‐MOOC courses were not available free of charge and had selective entry requirements and closed licensing. MOOCs’ availability for free and unconditional admission presents an opportunity for widening access to education. However, open licensing and interoperability is still needed to expand this opportunity globally.

5. Availability The concept of availability refers to both how many hours per day the courses are available to the students as well as the timely availability of the courses, teachers and facilities such as books and classrooms. MOOCs and other online courses were available 24 hours a day during their run time. However, the huge number of MOOCs’ students hinders tutors’ responsiveness. In LSIO, for instance, a wrong link to assignment submission was posted. Despite the forum discussion on this issue, the course team could not notice this mistake before the submission deadline. Fortunately, the course team offered a new deadline to students who had not submitted their work. This difficulty did not occur in e‐Academy, UKOU and EMU courses. However, the shortage of teachers and facilities caused many irregularities in the NUR courses. We sometimes walked for 40 minutes to the classroom for sessions that were eventually cancelled because teachers or classrooms were not available. The experience was similar when it came to getting a book from the university library. Hopefully, open licensing is globalising the access to learning materials but the digital divide still needs to be addressed.

6. Diversity A course’s diversity refers to variety in settings of practices covered in the learning materials, course participants, learning activities and assessment. In terms of the settings in the course content, the VOA and BBC learning was globalised, thanks to content that referred to practices and events from various parts of the world. The UKOU, e‐Academy courses and OCL4Ed content also covered practices in many parts of the world. The content of the AIP and IHTS covered practices in Europe and the USA. As for LSIO and most EMU courses, the contents were set in North American context alone. The massive number of MOOC participants includes those from diversified origins, expertise and experience. The contribution of these students alleviates the shortcomings in MOOCs’ diversity of settings of practices. In the three xMOOCs I analysed, students formed virtual study groups based on their geographical locations and discussed the content and related practices from their cultural perspectives. This opportunity may be absent for students who are taking online or face‐to‐ face classes in a dominant cultural setting. In terms of the variety of activities, LSIO leads with activities that included watching videos, forum discussion, reading materials, weekly innovation constraint diagnosis, reflection on innovation constraints, quizzes, peer grading and group projects. All these activities contributed to the course assessment. Learning activities were also diverse in the UKOU, e‐Academy and EMU courses since learning could happen via course reading, discussion with peers, course lectures and writing various assignments.

7. Delivery I analysed course delivery in terms of the quality of lectures, assessment and empowerment (Lane 2009, Lane and Van‐Dorp 2011). The quality of xMOOC lectures was high and some of these MOOCs are as engaging as the UKOU, EMU and e‐Academy courses. While many critics argue against xMOOCs by claiming that these courses are based on the behaviouristic approach, the forum discussion and critical writing opportunities were offered in IHTS as optional and required in LSIO. LSIO was the most social constructivist of all the ten cases and included reflection and peer feedback. Unlike many of my previous face‐to‐face and online courses, the student’s freedom to choose how and when to engage with the course is not undermined in MOOCs. In these courses, students decide at which level they engage with the courses and when they work on their various learning activities. This makes MOOCs more flexible than other courses. Assessment in AIP and IHTS consisted mainly of multiple choice questions (MCQs) except for those students who pursued the performance track in the former MOOC and those who wrote essays for extra‐credit in the latter. MCQs also dominated formative in‐lecture quizzes in all three xMOOCs. Feedback to students was

343

Bernard Nkuyubwatsi automated. LSIO tried to incorporate in‐lecture questions that would allow students to write their thoughts but anything submitted received a “correct!” response even when it was meaningless and unrelated. Nevertheless, quizzes that contributed to the course final grade did not suffer from this problem. Most of the assignments that counted for the final grade were peer‐assessed in the light of a rubric developed by the instructor. Using this rubric provided a common yardstick, which minimized divergence that could be caused by cultural and grading system differences. Feedback in xMOOC assessment was speedy and timely. Despite limitations in the three xMOOCs’ assessment and related feedback, these courses were much better assessed than the NUR ones. It could take up to two months to get exam results and the only feedback was a grade for each answer and a total score in “15/20” format. In some NUR course, we received no feedback and knew our grades only when final results were posted on the faculty or department notice boards or windows. The feedback on EMU, UKOU and e‐Academy course assignments and exams was not instant but it was timely and meaningful. IHTS and LSIO lead in terms of student empowerment. This empowerment relates to the course’s ability to restore the student’s self‐confidence that had been distorted by factors such as geographical remoteness, various filters used for students’ admission in selective education, students’ social economic disadvantages and the digital divide (Lane 2009, Lane and Van‐Dorp 2011). These authors argue that those factors lead to students’ self‐perception as being not good enough for a course. The empowerment occurs in student admission, course delivery and assessment. The IHTS and LSIO were highly empowering since it was possible to score 10 out of 10 in quizzes. While the quiz grades are not more important than learning, the “I can make it!” or “epiphany” feeling triggered by a high score encourages less confident students to stick with learning instead of dropping out. Other highly empowering courses were from the BBC, the VOA and the e‐Academy. The AIP’s final grade was entirely based on the end‐of‐course summative exam for the awareness and foundation levels, which made the course disempowering. The admission process for EMU, NUR and other selective courses is greatly disempowering. However, EMU courses’ delivery was empowering because all courses ran as planned and provided study guides as well as assignment guides. The delivery of NUR courses was disempowering because there were neither study guides nor assessment guides and class sessions were sometimes cancelled without notice.

8. Interaction I compared interaction in the four MOOCs with that in the other six case units in the light of Moore’s (1989) discussion of student‐teacher, student‐student and student‐content interactions. Interaction with the teacher is minimal in both the cMOOC and xMOOCs and was completely absent in my radio learning. It was maximized in the EMU courses but relatively low in the NUR ones due to a huge class size and overwhelming teachers’ workload that hampered their contact with individual students. Interaction with peers was optional in the xMOOCs with the exception of LSIO which required and graded participation in the forum discussion for all students and group work for those students enrolled in the studio mastery track. As for OCL4Ed, interaction with peers could be maximized, but was quite difficult to handle and time consuming due to a vast number of posts. I had to spend twice as much time as the time suggested by the facilitators to follow the logical flow of the discussion. I had already dropped this course in its earlier offering after noticing that the actual course workload is far beyond the facilitators’ estimate. This time, I was ready to spend much more time to avoid dropping the course the second time. This is similar to xMOOCs forum discussion, but these courses enable maximising learning through other channels such watching lecture videos, engaging with required and recommended readings, course assignments and other activities. For a better orientation in the forum discussion, various threads were linked to specific course sections in xMOOCs. This practice saved students’ time that would otherwise be wasted trying to identify which thread relate to a specific course chapter. Interaction with content was also maximized in the radio learning. However, the limited access to content at NUR hindered the maximal benefit from this type of interaction. A significant amount of time was lost in attempting to access the content.

9. MOOCs’ potential contribution to education improvement in Rwanda Various aspects of xMOOCs can be built on to widen participation and reinvigorate quality in Rwandan higher education. First, these courses are open in terms of being free and accepting students without entry requirements. In this way, they can alleviate the financial constraints that restrict accessibility and quality of Rwandan higher education. Second, xMOOCs are more scalable than conventional higher education courses. This scalability offers an opportunity for mitigating the shortage of higher education teachers in Rwanda. Third,

344

Bernard Nkuyubwatsi xMOOC participants are from various corners of the globe and have a diversity of expertise and experience. This aspect could help Rwandan students develop their multicultural literacy. Fourth, the assessment method used in many xMOOCs is empowering, which might encourage less confident students to stick with the courses. For this reason, xMOOCs could help restore Rwandan students’ self‐confidence vis‐à‐vis western education. Finally, interaction with content can be maximized in xMOOCs and that can lead to meaningful learning as Anderson (2003) argues. Maximizing interaction with MOOC content can improve the learning of Rwandan tertiary education students who do not have adequate access to educational content. Since cMOOCs are not based on a didactic pedagogy as are other courses in Rwandan higher education, they do not fit in the current formal system of this level of education. However, they can contribute to network creation and development for academic staff and advanced students. This would enable them to share their learning and professional practices globally and to engage in lifelong learning. However, there are constraints alongside the MOOC opportunities. Reliable broadband connectivity which is essential for taking MOOCs is not available to most Rwandans. Miniwatts Marketing Group (2012) estimates the Internet penetration in this country at around 7 percent. Most of those Rwandans who access the Internet only do via poor connectivity that cannot handle the xMOOC lecture videos. Internet failure was the biggest constraint during my UKOU and e‐Academy courses, which were not as heavily loaded with videos as xMOOCs are. While most Rwandan learners have access to radio and mobile phones, the xMOOCs content is not compatible with these technologies. Moreover, the licensing of most xMOOC contents does not allow the reuse, remix, repurposing and redistribution. All these constraints are coupled with the lack of open educational practices in Rwanda which would enable appropriate assessment, certification and accreditation for MOOC students.

10. Conclusion In this paper, I have attempted to compare and contrast MOOCs with my own radio, online and face‐to‐face learning experience. I have also discussed, briefly, MOOCs’ potential in improving the quality of and access to higher education in Rwanda. I found MOOCs to be the most open courses although they are not open in all respects. MOOCs are flexible and available 24 hour a day during their run time but the tutors’ availability to their students is very low. The recruitment, delivery and assessment modes in xMOOCs are empowering. Interaction with xMOOC contents can be maximal and supplemented with interaction with peers to lead to meaningful learning. LSIO, an xMOOC, was the most social constructivist course of all the ten cases I analysed in this study. xMOOCs can contribute to mitigating financial constraints and the shortage of higher education teachers in Rwanda. They can also help in the development of multicultural literacy and empower students and learners in Rwanda. As for cMOOCs, they can help academic and advanced students develop networks with their global counterparts. However, various constraints still stand in the way, notably the low level of Internet ubiquity and reliability. MOOCs also lack interoperability with technologies that are widely available in Rwanda, and enabling open educational practices are not yet adopted in that country. The findings in this study should not be generalized in any way since learning experience is unique to each individual learner and the four MOOCs cannot be considered representative of all courses in this category. I must take into account my personal bias toward interaction with the content. However, I acknowledge that an exclusive focus on any single type of interaction restricts the learning achievement. Maximizing learner‐ content interaction and supplementing or balancing it with other types of interaction that are possible in each context could help open up education opportunities globally and more specifically in Rwanda. Academics and educational decision makers in Rwanda could themselves experience xMOOCs and, through them, possibly create opportunities for learners who wish to study but are not served by the current higher education system. This could help in the development of a socio‐economically inclusive higher education to transform the country into a knowledge‐based society.

Acknowledgements I am profoundly indebted to Professor Gráinne Conole, Professor David Hawkridge, Dr Palitha Edirisingha and the reviewers for their constructive comments on drafts of this paper.

References Anderson, T. (2013) Promise and/or peril: MOOCs and open and distance learning, http://www.col.org/SiteCollectionDocuments/MOOCsPromisePeril_Anderson.pdf (accessed 01 May 2013).

345

Bernard Nkuyubwatsi Anderson, T. (2003) “Getting the mix right again: An updated and theoretical rationale for interaction”, International Review of Research in Open and Distance Learning, Vol 4, no. 2, available from http://www.irrodl.org/index.php/irrodl/article/view/149/708 (accessed 02 May 2013). Armstrong, A. (2012) Coursera and MITx ‐ sustaining or disruptive?, Changing Higher Education, http://www.changinghighereducation.com/2012/08/coursera‐.html (accessed 31 January 2013). Bates, T. (2012) What’s right and what’s wrong about Coursera‐style MOOCs, http://www.tonybates.ca/2012/08/05/whats‐right‐and‐whats‐wrong‐about‐coursera‐style‐moocs/ (accessed 31January 2013). Daniel, J.S. (2012) “Making sense of MOOCs: Musing in a maze of myth, paradox and possibility”, Journal of Interactive Media in Education, available from http://www‐jime.open.ac.uk/article/2012‐18/pdf (accessed 21 December 2012). Edmundson, M. (2012) The trouble with online education, The New York Times, July 19, 2012 http://www.nytimes.com/2012/07/20/opinion/the‐trouble‐with‐online‐education.html (accessed 11 February 2013). Fini, A. (2009) “The technological dimension of a Massive Open Online Course: The case of the CCK08 course tools”, International Review of Research in Open and Distance Learning, vol 10, no.5, available from http://www.irrodl.org/index.php/irrodl/article/view/643 (accessed 06 May 2013). FutureLearn (2013) Introduction from Simon Nelson, launch CEO Futurelearn, http://futurelearn.com/feature/introduction‐from‐simon‐nelson‐launch‐ceo‐futurelearn/ (accessed 07 April 2013). Koller, D. (2012) What we're learning from online education, TED, http://www.ted.com/talks/daphne_koller_what_we_re_learning_from_online_education.html (accessed 10 February 2013). Kolowich, S. (2012) MOOCs for credit, Inside Higher Ed http://www.insidehighered.com/news/2012/10/29/coursera‐strikes‐mooc‐licensing‐deal‐antioch‐university (accessed 10 February 2013). Lane, A. (2009) “The impact of openness on bridging educational digital divides”, International Review of Research in Open and Distance Learning, Vol 10, no. 5, available from http://oro.open.ac.uk/24791/1/IRRODL_2009.pdf (accessed 11 January 2013). Lane, A. and Van‐Dorp, K.J. (2011) Open educational resources and widening participation in higher education: Innovations and lessons from open universities. In: EDULEARN11, the 3rd annual International Conference on Education and New Learning Technologies, 04‐05 July 2011, Barcelona, available from http://oro.open.ac.uk/29201/1/OPEN_EDUCATIONAL_RESOURCES_AND_WIDENING_PARTICIPATION_andy.pdf (accessed 14 January 2013). Levy, D. and Schrire, S. (n.d.) The Case of a Massive Open Online Course at a College of Education, available from http://conference.nmc.org/files/smkbMOOC.pdf (accessed 11 February 2013). Mackness, J., Mak, S. and Williams, R. (2010) The ideals and reality of participating in a MOOC. In: Proceedings of the 7th International Conference on Networked Learning 2010. University of Lancaster, Lancaster, pp. 266‐275. ISBN 9781862202252, available from http://www.lancs.ac.uk/fss/organisations/netlc/past/nlc2010/abstracts/PDFs/Mackness.pdf (accessed 06 May 2013). Mazoue, G.J. (2013) The MOOC model: Challenging traditional education, Education Review Online Jan/Feb 2013, http://www.educause.edu/ero/article/mooc‐model‐challenging‐traditional‐education (accessed 10 February 2013). Miniwatts Marketing Group (2012) Internet usage statistics: The internet big picture, http://www.internetworldstats.com/stats.htm (accessed 28 February 2013). Moore, G.M. (1989) “Three types of interaction”, American Journal of Distance Education, Vol 3, No. 2, available from http://www.gwu.edu/~ed220ri/reading/Moore_Interaction.pdf (accessed 24 May 2013). Pérez‐Peňa, R. (2012) Top universities test the online appeal of free, The New York Times, July 17, 2012, http://www.nytimes.com/2012/07/18/education/top‐universities‐test‐the‐online‐appeal‐of‐free.html?_r=1& (accessed 11 February 2013). Ripley, A. (2012) College is dead. Long live college!, Time Magazine http://nation.time.com/2012/10/18/college‐is‐dead‐ long‐live‐college/print/ (accessed 10 February 2013). Rodriguez, C. O. (2012) “MOOCs and the AI‐Stanford like courses: Two successful and distinct course formats for Massive Open Online Courses”, European Journal of Open, Distance and eLearning, available from http://www.eric.ed.gov/PDFS/EJ982976.pdf (accessed 20 February 2013). Shaw, C. (2012) FutureLearn is UK's chance to 'fight back', says OU vice‐chancellor, The Guardian, Thursday 20 December 2012, http://www.guardian.co.uk/higher‐education‐network/blog/2012/dec/20/futurelearn‐uk‐moocs‐martin‐bean (accessed 20 February 2013). Siemens, G. (2005) "Connectivism: A learning theory for the digital age", International journal of instructional technology and distance learning, available from http://itdl.org/Journal/Jan_05/article01.htm (accessed 30 March 2013). Thrun, S. (2012) Higher education 2.0, http://www.youtube.com/watch?feature=player_embedded&v=SkneoNrfadk (accessed 15 February 2013).

346

In the Presence of Technology – Teaching in Hybrid Synchronous Classrooms Anne‐Mette Nortvig Institute of Humanities, Department of Learning and Philosophy, University of Aalborg Copenhagen, Denmark amn@learning.aau.dk Abstract: In a hybrid synchronous classroom that includes both on line and on campus students, technology plays a big part in the teaching. This paper attempts to analyse and discuss how the transparency of technology affects opportunities for the students to interact and for the teacher to be present in different hybrid settings. In order to do so, the paper focuses on three concepts of the human‐technology relation. The first is embodiment of technology i.e. when technology almost transparently extends and/or enhances the human perception. The second concept, technological transformation of teaching, is found in the study in the video recorded lectures when e‐students are deprived of the traditional student behaviour as interacting with other students and the teacher in the same room. They therefore translate and interact with the recorded lesson not as process but as digital artifact. The last concept is concerned with the influence of technology in the classroom on‐campus seen in the teacher’s perspective when the technology is visible as a camera placed in front of him and the teaching is recorded and distributed to the e‐students at home. This setting makes him very aware of his teaching performance, but in order to avoid interference in the traditional and preferred face‐to‐face teaching, it causes him to act as if the camera wasn’t there. To categorise this experience of being in two places – on campus and online – at the same time without being fully present anywhere, the paper introduces the concept of disembodied presence and presents different learning design experiments that have been developed in a physiotherapy e‐learning program to deal with the experience of disembodied presence and to thematise the role of technology. Keywords: e‐learning, social presence, physiotherapy, desktop videoconferencing, human‐technology relation

1. Introduction To be present in a classroom can be enacted and described in several ways, and it is not necessarily the same thing as being physically present. Short et al. (Short, Williams, & Christie, 1976) defined social presence in the context of telecommunications as “the degree of salience of the other person in the interaction and the consequent salience of interpersonal relationships...” (p. 65). Here salience meant the degree to which a person is perceived as a real person in mediated communication (Pugsley, 2010) and they considered the medium itself to establish more or less social presence according to its ability to transmit nonverbal social cues. However, in the Internet age this distinction soon changed, and social presence became less about the objective qualities of the medium and more about perception (Borup, West, & Graham, 2012), and the concept of social presence and its relation to teaching and learning has been further developed. Definitions of social presence as the feeling of belonging to a group (Swan & Shih, 2005) and being able to interact with other students (Dziuban & Moskal, 2001) is also discussed in the research area. Some research is concerned with the fact that students are more satisfied with their online courses if they feel they belong there and can interact (Hartman & Truman‐Davis, 2001) and some suggest that the use of technology is balanced with a human touch of a real person (Borup et al., 2012). However, studies also find that students express satisfaction with web‐based lectures without opportunity of interaction (Gosper et al., 2007) that social presence is not always related to learning outcome (Beaudoin, 2002) and that increased social cues can be a hindrance for learning especially to students with low technological efficacy (Lyons, Reysen, & Pierce, 2012). Since the social presence of the teacher/instructor is found to have a larger impact than students’ social presence on e.g. perceived learning, it is suggested that research could be further elaborated in the area concerning the teachers’ social presence (Lowenthal & Lowenthal, 2010). In e‐learning settings, the teachers’ academic identity is found to be changing (Hanson, 2009) Although teachers might not be quite ready to embrace the ‘disembodiment’ or ‘re‐positioning’ required by e‐learning (Hanson, 2009) some suggest that they have to become accustomed to ‘(dis)embodiment’ as an important milestone in learning more about student learning in e‐learning settings (Taylor, Lopez, & Quadrelli, 1996). These aspects are at stake in the hybrid synchronous classroom and will be addressed in this paper.

347

Anne‐Mette Nortvig

2. The physiotherapy e‐learning case When e‐learning is introduced in professional bachelor programs with a strong tradition of mixing dialogues and physical and practical exercises with the classroom lectures, the role of the teacher and the opportunities for the students to participate actively in the teaching slowly change: Students are no longer only young people sitting in the classroom with the teacher, but they can also be invisibly located at home in front of a computer screen and represented by a steady camera in the classroom as was the case in the physiotherapy e‐ learning program in Denmark. The e‐students in this study could choose to participate synchronously in the teaching while it was taking place on campus and was being recorded, or they could choose to watch the video recorded lecture asynchronously afterwards when it had been edited by the teacher. Thus, present in the classroom were both physically present students and students attending class virtually. This setting is sometimes referred to as Global Classroom but as the case in question here is not global and it is the hybrid space, time, and presence that is in focus, I will here refer to the hybrid setting that is taking place on campus with e‐learners at home connected to the classroom as a hybrid synchronous classroom. The empirical basis of the paper is a PhD project’s findings during 1,5 years fieldwork in professional bachelor program in physiotherapy when e‐learning (or more accurately blended learning) was just being introduced for the first time in this program in Denmark. The qualitative data was constructed through participant observation in the teaching on campus and on line in the hybrid synchronous classrooms and through interviews with e‐students in 5 focus groups and with their teachers in semi structured life world interviews. All of these revolved around experiences and thoughts behind designs for learning in these hybrid settings. The paper focuses on only a part of the overall PhD‐project, which aims to investigate the role of e‐learning in development of professional identity in professional bachelor program with a case in physiotherapy.

3. Embodiment of technology ‘Without this opportunity of e‐learning where I get the lectures without showing up on campus, I would be unable to become a physiotherapist!’ The e‐students in professional bachelor program in physiotherapy are often older than the average student, they do not live near campus, and they have a family and a job beside their studies. Thus, e‐learning affords a way for the students to become physiotherapists despite the different obstacles; it makes the impossible possible. A blind man can make use of a cane to ‘see’ the world, to notice doorways, staircases and chairs, but he does not pay attention to and is not necessarily interested in the cane as such: he uses it to get in contact with and get information about the world. Technology is said to be embodied (Ihde, 2010) when it mediates the perception of the world, as the visually impaired man uses technology (glasses) as an enhancement of bodily perception, or when a previously impossible bodily perception or action is made possible due to technology ‐ and the blind man sees the world. In Don Ihde's words concerning the embodiment relation 'I take the technologies into my experiencing in a particular way by way of perceiving through such technologies and through the reflexive transformation of my perceptual and body sense.' (Ihde, 2010, p.135). The e‐students in physiotherapy use e‐learning and in this case the desktop videoconference tool Adobe Connect to get inside the classroom where the lecture is taking place and to get in contact with the teacher and optionally also with the students present at campus. They embody the digital technology by letting the camera and microphone replace their eyes and ears, and the Adobe Connect chat functionality become their voice in the classroom. The students are physically present at home but virtually and socially present on campus. Therefore and by the use of technology, the e‐student can interact with the teaching. Following Ihde (ibid.) the relation can be visualised like this: (Student – technology) – teaching Thus, technology becomes part of the way the students experience the teaching, but this embodied human‐ technology relation requires the transparency of technology: ‘The interface of a telepresence system is highly mediated and yet is supposed to be transparent, in the sense that it should transmit a view to the human operator and allow the operator to interact ‘naturally’ with what she sees’ (Bolter & Grusin, 1999).

348

Anne‐Mette Nortvig By the same token, Lombard and Ditton (Lombard & Ditton, 1997) defined presence as the perceptual "illusion of nonmediation". This "illusion” occurs when a person fails to perceive or acknowledge the existence of a medium in his/her communication environment and responds as he/she would if the medium were not there (Picciano, 2002). Only if the sound suddenly lacks or the picture is missing, the e‐students’ focus shift from the content of the teacher’s lecture to the mediation of it. As Bruno Latour puts it (Latour, 1999): ‘Take, for instance, an overhead projector. It is a point in a sequence of action (in a lecture, say), a silent and mute intermediary, taken for granted, completely determined by its function. Now suppose the projector breaks down. The crisis reminds us of the projector’s existence.’ However, the technological breakdown also reminds the teacher in the classroom of the e‐students’ existence: ‘Kenneth, we can’t hear you!!!’, ‘Now where’s the sound again?’ popped up immediately in the chat every time the sound disappeared. Due to technical problems, the e‐students sometimes got excluded for several minutes until the technology was fixed and slipped back into transparency and let the teaching continue. These technological problems caused a lot of frustration to the e‐students, so instead of participating in the lecture synchronously, some of them chose to watch the recorded version of the lecture ‐ where pauses, breakdowns and time for group work had been cut out. So when technology worked and was transparent, the lecture in the classroom could take place almost as if there were no e‐students participating. When for example, theory concerning physiotherapy was lectured, the e‐students sitting at home could participate by listening and looking at the teacher and they had the opportunity to ask or answer questions in the chat as if they were sitting in the same room. Nevertheless, findings in the study show that the e‐students did not interact actively in the teaching. Very often the chat was silent, and although technology was a way for the students to access the classroom, it also excluded them when the chat was not visible on the screen in the classroom or the lacking or missing sound was unnoticed by the teacher. But when it broke down – and it did very often during the fieldwork period – the transparency of the technology stopped, the e‐students’ experience of embodiment of technology ceased, and the teacher was clearly reminded of the presence of his e‐students.

4. Technological transformation of teaching In traditional classroom teaching, the teacher and the students are in the same physical place at the same time, what takes place is a process that is not meant to be repeated, and the activities are often evaluated immediately. Similarly, the teacher’s and the students’ actions and interactions are normally coordinated, and not only are they able to see each other, but they also perceive themselves in the room and are seen by others. Thus, the body is present both as perceived and perceiving. This reversibility of perception (Merleau‐Ponty, 1969) is a feature of shared physical spaces. However, the experience of being perceived by others is much less marked in virtual spaces such as the on line desktop videoconference teaching. In the hybrid synchronous classrooms, the e‐student’s body is only perceived as a name on the screen, and it is completely non‐existent in the video‐recorded version of the teaching: the student perceives the teacher but he himself is not perceived by others because he only watch the lesson later on but did not participate in it when it took place on campus. Thus, when the e‐students watched the video recorded version, their experience of the learning situation changed completely: the teacher was in another room and the e‐student was unable to interact with him/her and with the other students in the classroom whom he/she could not see. However, what the teacher had said could be repeated over and over again, put on fast forward or paused if the student chose and had the time to do. Moreover, findings in the study showed, that all the e‐students watched the recorded lectures always or nearly always and they felt that they learned a lot from it (Borup, West, & Graham, 2012; Gosper et al., 2007; Jones, 2011) The question is then how the e‐student handles a situation where he/she is not physically present, where the reversibility of perception is missing and where immediate interaction is impossible. As a student in one of the focus groups puts it: ‘it is difficult. That teeny tiny picture dedicated to the teacher and the big picture of the PowerPoint… I would really like it the other way around.’ Another one added: ‘and you can’t just ask the guy next to you if you don’t get it. I would prefer to be there while it was being recorded.’ Ihde (Ihde, 2010) gives an example of a man sitting inside his living room while looking at the thermometer showing that it is very cold outside. Without the bodily experience but through the interpretation of the information he receives from technology, he now knows that it is cold outside as if he had been outside and felt the cold. The video recorded version of the physiotherapy teaching provides similar things: Without

349

Anne‐Mette Nortvig physically sitting in the classroom and feeling the atmosphere and the presence of the others, and without having the opportunity to “just ask the guy next to you” or the teacher, the e‐student have to interpret it as if she herself is being taught, if she wants to learn and not just observe the other students being taught. Thus, the e‐student has access to the classroom teaching and can interact with technology but not with the teaching that is taking place. If we follow Ihdes again, it can be visualised like this: Student – (technology – teaching) But not only does technology make the students translate the teaching, it also transforms it. Findings in the fieldwork showed that the students compare the teaching to the reading of texts (as artifacts) instead of teaching (as process): ‘It [to watch the video recorded lecture] is better that reading the texts yourself.’ ’The [teaching] that isn’t as important as pathology, you could put it on the net and then read it yourself’. The video recorded lecture is a storable artifact with an opportunity for the e‐student to interact with the technical quality such as the teachers speaking pace or volume, and if the student has difficulties with understanding the content, he or she can chose for example to repeat it or pause it. Thus, when teaching is transformed into an artifact, technology is more visible than in the above‐discussed synchronous videoconference teaching, and despite the lack of experience of social presence, the e‐students still find that they are learning because of the possibility of interacting with the content via technology in the video‐ recorded. The table below shows the many parameters that are affected by technology in the two settings with different consequences: Table 1: Comparison of video conference teaching live and recorded

Videoconference teaching, live

Participation Transparency of technology Reversibility of perception Interaction with Time Storable Function as Resemblance to

By listening, asking/answering questions Yes, preferably Partly. Student is perceived as name (in this case) or as face Teaching Same No Process Classroom lecture

Recorded videoconference teaching Listening and watching No No Technology Different Yes Artifact Textbook text

However, this visibility of technology in the hybrid synchronous classroom affects the teacher differently than it does the students.

5. Disembodied presence of the teacher in the hybrid synchronous classroom Especially in the beginning, the lack of the traditional reversibility of perception seemed to be confusing to the teachers in physiotherapy program. ‘I’m still not used to seeing myself on video’ a teacher said, and later he elaborated: […] In the beginning, I taught differently because of the camera, but I don’t anymore; I think I have got used to it standing there. In the beginning I was very aware of the technology, including the way I sat and spoke […]’. What seemed to be more or less transparent from the e‐students point of view is very visible from the teacher’s perspective: The camera is placed right in front of the him, and especially when the teacher is new to e‐learning he pays a lot of attention to it (Pugsley 2010). However, in order not to let it disturb the traditional teaching where the teacher addresses the students sitting with him in the classroom, he tries to keep his focus in the room, he looks at the students with him and he is aware of their perceiving of him and their response to his teaching. As a teacher said: ‘I don’t communicate with them [the e‐students at home] with my face, because it would take the focus away from the students sitting there [in the classroom]… But I am very aware of them so that they don’t feel as if they are just sitting on the side‐lines.’ Thus, the teacher is aware of not letting his conscious awareness concerning the e‐students be noticed by the students in the classroom, and in the same manner, he thinks that he must be aware not to pay too much attention to the technology standing in front of him. But in order to make the technology as transparent as possible to the e‐

350

Anne‐Mette Nortvig students and to the students on campus, he also has to be aware of the presence of the camera because it represents the e‐students eyes and ears in the classroom. ‘You really need big reserves of energy to be present both in the classroom with the students and pay attention to the chat and to answer the e‐students without making the on‐campus students think: ‘Well, now I’m just wasting my time…’ So I think it’s really ‐ to be present in the classroom and to the e‐students ‐ I think it’s really difficult; I haven’t been able to do it.’ The general feeling among the teachers was that the more social presence they invested in the e‐students the less they seemed present on campus. Thus, the role of the teacher and his perception of the situation is changing from a traditional focused physical presence in the classroom switching between monologues and dialogues with the students in the classroom to what might be called a disembodied presence. The teacher is present in two places at the same time without being really present anywhere (Gosper et al., 2010; Hanson, 2009). One of the teachers said: ‘It’s damn hard! You live in two worlds!’ and one of her colleagues: ‘It feels like sitting between two chairs! […] It’s a compromise but nobody is really happy.’

Figure 1: Classroom setting with the camera in front of the teacher and with the e‐students' chat behind him. The teacher's focus is mainly at the students in classroom but also ‐ thought in a hidden way ‐ at the camera and at the chat, which represent the e‐students

6. Experience of disembodiment as point of departure for new designs for learning In the last period of the fieldwork, the teachers had created different learning design experiments (of which only a few of them will be discussed here). In order to overcome the problem concerning especially the experience of disembodied presence, some of the teachers separated their teaching by focusing on one group at a time: when the camera was turned off, they lectured in the traditional way in the classroom with the opportunity of physical presence. Moreover, they could shift between lectures, dialogues and practical exercises when needed as they had always done and preferred to do. In order then to teach the e‐students, one teacher made special podcast lectures after the teaching of the students on campus. In the podcasts, she spoke directly to the e‐students and presented the content of the lesson though in a shorter and more compressed way. ‘The students can, you know, repeat it, if it’s a bit too compressed […] But I just do it for the sake of their bright eyes. I don’t get extra hours for the extra work […] But I cannot in decency do otherwise.’ (cf. Borup et al., 2012; Jaggars, Edgecombe, & Stacey, 2013) . Another learning design focused on the fact that the e‐students and the teacher were located at different places so he invited them to group counselling in Adobe Connect. In this way the traditional teaching was turned away from the monologue to the dialogue and often with a point of departure in the students problems, interests and chosen focus. In this learning design, the teacher tried to keep the technology transparent and thematised the embodiment relation in the telepresence meeting. He spent time together with the students in counselling although they were physically apart. Yet another example of learning design experiments considered the opportunity of a doubled reversibility of perception in e‐learning as an advantage. In this case, the teacher encouraged the students to work with video production of their transfer of patients, and afterwards they were to examine their own and their fellow students’ videos in order to discuss different perspectives and e.g. good and bad ways to do it (Tripp & Rich, 2012). Merleau‐Ponty finds that a person can only imagine what he or she looks like from the outside; (for example, a lot of people do not recognize their own hands on a picture because the body is experienced from

351

Anne‐Mette Nortvig the inside as lived body and very rarely objectified to itself (Merleau‐Ponty, 1969) Thus, he distinguishes between the phenomenological body, which is the body that I live and sense; and the objective body i.e. as it appears to others. This distinction is thematised in this last learning design when in the video; the recorded phenomenological body is transformed into an objective and acting body that can be seen and reflected upon by the student himself and the other students. Thus, if technology is made visible by using it consciously in a learning design, the objectification of own body is possible ‐ and desirable especially in professional programs like physiotherapy where knowledge about own acting body is very important: the student gets the opportunity to see himself as acting and actively perceiving body as with the eyes of another (Cooley, 1992).

7. Conclusion The focus of this paper have been on the different ways technology affect the teaching in a so‐called hybrid synchronous classroom when a webcam is put in front of the teacher in order to broadcast and record the teaching taking place. It has been argued that technology plays different roles depending on

Whether it is transparent and thereby establishes an embodied relation between student and technology which can be the case in telepresence meetings or video conference teaching,

Or technology seems less transparent, which is the case when the lecture is recorded. Thereby it can contribute to a transformation of the teaching from the traditional understanding of it as a process to a learning artifact that can be interacted with,

Or whether technology is anything but transparent which can be the case from the teacher’s perspective in the hybrid synchronous classroom. It can then cause an experience of disembodied presence and urge the teachers to experiment with different learning designs in order also to try to overcome the challenge that consists of the e‐students potentially invisible presence in the presence of technology.

References: Beaudoin, M. F. (2002). Learning or lurking?: Tracking the “invisible” online student. The Internet and Higher Education, 5(2), 147‐155. Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding new media. Cambridge, Mass.: MIT Press. Borup, J., West, R. E., & Graham, C. R. (2012). Improving online social presence through asynchronous video. The Internet and Higher Education, 15(3), 195‐203. Cooley, C. H. (1992). Human nature and the social order Transaction Books. Dziuban, C., & Moskal, P. (2001). Emerging research issues in distributed learning. Orlando, FL. , the 7th Sloan‐C International Conference on Asynchronous Learning Networks Gosper, M., McNeill, M., Woo, K., Phillips, R., Preston, G., & Green, D. (2007). Web‐based lecture recording technologies: Do students learn from them? Gosper, M., McNeill, M., Phillips, R., Preston, G., Woo, K., & Green, D. (2010). Web‐based lecture technologies and learning and teaching: A study of change in four australian universities. Research in Learning Technology, 18(3) Hanson, J. (2009). Displaced but not replaced: The impact of e‐learning on academic identities in higher education. Teaching in Higher Education, 14(5), 553‐564. Hartman, J., & Truman‐Davis, B. (2001). Factors relating to the satisfaction of faculty teaching online courses at the university of central florida. Online Education, 2, 109‐128. Ihde, D. (2010). A phenomenology of technics. (Hanks, Craig ed., pp. 134‐155). West Sussex, UK: Wiley‐Blackwell. Jaggars, S. S., Edgecombe, N., & Stacey, G. W. (2013). Creating an effective online instructor presence. Community College Research Center, Columbia University, Jones, I. M. (2011). Can you see me now? defining teaching presence in the online classroom through building a learning community. Journal of Legal Studies Education, 28(1), 67‐116. Latour, B. (1999). Pandora's hope: Essays on the reality of science studies. Cambridge, MA; London, UK: Harvard University Press. Lombard, M., & Ditton, T. (1997). At the heart of it all: The concept of presence. Journal of Computer Mediated Communication, 3(2), 0‐0. Lowenthal, D., & Lowenthal, P. (2010). A mixed methods examination of instructor social presence in accelerated online courses. Annual Meeting of the American Educational Research Association, Denver, CO, Lyons, A., Reysen, S., & Pierce, L. (2012). Video lecture format, student technological efficacy, and social presence in online courses. Computers in Human Behavior, 28(1), 181‐186. Merleau‐Ponty, M. (1969). Phénoménologie de la perception. Paris: Gallimard. Picciano, A. G. (2002). Beyond student perceptions: Issues of interaction, presence, and performance in an online course. Journal of Asynchronous Learning Networks, 6(1), 21‐40.

352

Anne‐Mette Nortvig Pugsley, M. (2010). An exploratory study of teacher perception of social presence: Design and instructional practices for new online teachers. ProQuest LLC, Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. London: John Wiley and Sons Ltd. Swan, K., & Shih, L. F. (2005). On the nature and development of social presence in online course discussions. Journal of Asynchronous Learning Networks, 9(3), 115‐136. Taylor, P. G., Lopez, L., & Quadrelli, C. (1996). Flexibility, technology and academics' practices: Tantalising tales and muddy maps Citeseer. Tripp, T., & Rich, P. (2012). Using video to analyze one's own teaching. British Journal of Educational Technology, 43(4), 678‐704.

353

Searching for the Ideal CLIL Course Design Jarmila Novotná and Lenka Procházková Charles University in Prague, Faculty of Education, Prague, Czech Republic jarmila.novotna@pedf.cuni.cz lenka.tejkalova@gmail.com Abstract: The paper presents a process of optimization of a Content and Language Integrated Learning (CLIL) didactics course for Mathematics teacher‐trainees, starting from a traditional face‐to‐face course through an e‐learning project (Novotná and Tejkalová 2010) to a blended‐learning model. The process of the course design improvement is described; the current model is presented in detail, underlining the advantages of blended approach for CLIL: parallels between effective CLIL didactics and blended learning tools are discussed, methodological approach to course design is explained. The aim of this case study is to discuss the possibilities of blended learning for CLIL teacher training, offering the students' perspective based on complex feedback survey. The paper aims especially at teacher‐training course designers and CLIL practitioners. CLIL refers to situations where “subjects, or parts of subjects, are taught through a foreign language with dual‐focused aims, namely the learning of content, and the simultaneous acquisition of a foreign language” (Marsh and Langé 1999). For an effective CLIL class, teacher qualification is essential. The design of our course stems from current research on CLIL teacher‐training, national methodological handbook for e‐learning in higher education, and the syllabi of regular Didactics of Mathematics courses. We conclude that the CLIL teacher training course needs to expand the teachers’ didactic skills palette by teaching compensation strategies, focusing on multimodality, and incorporating language‐teaching strategies in Mathematics lessons. The course design is continuously developing based on latest experience; it has also become a compulsory subject for Mathematics teacher trainees. The blended model of the course allows the teachers to cater to the language aspect of CLIL more effectively, and especially to illustrate CLIL methodology on two levels: as the content of the course and also via the methods employed to teach this content. Blended learning seems to suit both Czech students and Erasmus students who take part in the course. Keywords: blended learning course, Content and Language Integrated Learning, CLIL, course development, teacher training, course design

1. Introduction Blended learning is seen as the blend of traditional teaching and technology based teaching using a wide variety of pedagogical methods and different forms. In its width it parallels Content and Language Integrated Learning (CLIL), where the methods employed are a blend of language and content teaching, and which also has incredibly varied forms. In this paper we present a blended learning CLIL course for teacher trainees and its development from traditional face‐to‐face setting through an e‐learning course. In our setting, we teach CLIL to future teachers of mathematics. The syllabi of these teacher trainees feature several general and specialized didactics courses, as well as courses in pedagogy, psychology, classroom management, etc. Our aim is to extend their training towards implementing a foreign language in their teaching and/or using the strategies of integrated learning to improve their teaching competences and repertoire. CLIL, Content and Language Integrated Learning, refers to situations where “subjects, or parts of subjects, are taught through a foreign language with dual‐focused aims, namely the learning of content, and the simultaneous acquisition of a foreign language” (Marsh and Langé 1999). Marsh et al. (2010) define eight target areas of professional competences of CLIL teacher training: Personal Reflection, CLIL fundamentals, Content and Language Awareness, Methodology and Assessment, Research and Evaluation, Learning Resources and Environments, Classroom Management and CLIL Management. Another major CLIL teacher‐ training framework, CLIL across Contexts (Milne, Llinares and Morton, 2010), defines eight areas of teacher‐ training for CLIL: Learner Needs, Planning, Multimodal Teaching and Learning, Interaction, Subject Literacies, Evaluation / Assessment, Cooperation and Reflection, Context and Culture. Both Marsh’s and Milne’s et al. lists are very general and aimed universally; our students are teacher trainees who cover many of the above‐ mentioned areas in other subjects. Independently from the general frameworks, several areas, skills and competences of a CLIL teacher had been identified as particularly relevant in our specific setting: Novotná, Hadj‐Moussová, and Hofmannová (2001) claim that "CLIL calls for an interactive teaching style. Verbal input should be accompanied with the use of visual and multimedia aids". Novotná and Hofmannová (2005) identify CLIL teacher competences, namely language, methodological and classroom management competences. Since

354

Jarmila Novotná and Lenka Procházková classroom management is covered in other didactic and pedagogical courses at the faculty, as is the general linguistic competence, our course mainly focuses CLIL methodology (and to minor extent, on specific mathematics terminology). As we strongly believe in experiential learning, we built the current course not only to cover CLIL methodology, but also to illustrate the approach directly: by the teaching methods employed in the training.

2. Course development: Face‐to‐face, e‐learning, blended model The current blended‐learning model stems from a 14‐year long tradition of CLIL teacher training at Faculty of Education, Charles University in Prague. Since the school year 1999/2000, there has been a special course to train CLIL teachers thanks to a unique cooperation between the Department of Mathematics and Didactics of Mathematics (DMDM) and Department of English Language and Literature (DELL). The primary aim was to educate teachers so that they were prepared and willing to use non‐traditional teaching strategies. The course was open mainly for students of English Language and a content subject ‐ mostly Mathematics (the Czech system of teacher training typically has the student choose two subjects in which they specialize, allowing for numerous combinations). The following items were highlighted as substantial: interaction of the three languages (L1: mother tongue, L2: foreign language and L3: the symbolic and graphical “language” of the specific subject), and differences in the work of a teacher when teaching in L1/L2 (Novotná and Hofmannová 2005). The face‐to‐face course covered the following areas: Content (non‐language) subject and its didactics when teaching in a foreign language (specific terminology, precision of the subject‐specific technical language, didactic approaches to teaching the non‐language subject through the medium of a foreign language), and foreign language didactics and didactics of CLIL (basic questions of bilingual education, methodological terminology for CLIL teaching, analysis of peer teaching focusing on cultural differences, making use of language‐teaching methodology in a non‐language subject) (Novotná and Moraová 2005). In 2009/2010, the first semester of the above‐mentioned course was implemented as e‐learning. It featured 10 units, including one lesson observation. The content of the units and tasks were as follows: 1.CLIL essentials: reading CLIL theory, watching short samples of CLIL lessons, discussing in an open forum. 2. Language in CLIL: watching an online webinar, writing a summary, commenting on further CLIL videos. 3. Bilingualism: studying theories of bilingualism, reading a sample case study and writing one. 4. CLIL stakeholders: reading and expanding on different views of Czech CLIL stakeholders (teachers, students, parents, directors of schools). 5. CLIL‐related approaches: comparing CLIL and similar approaches, focusing on methodology. 6. Preparation for lesson observation: reviewing CLIL specifics, commenting on lesson observation guidelines and checklists. 7. Real lesson observation at a Czech school: completing an observation checklist and interview with the teacher. 8. Lesson planning: commenting on lesson plans from a Czech CLIL project. 9. Text analysis: reviewing topics 2&5, analysing a text for subject specific terminology and general linguistic demands. 10. Open forum, formative test, feedback questionnaires. In the second semester (face‐to‐ face seminars), the students practised CLIL lesson planning and peer teaching. All individual tasks in the e‐ learning were commented upon and evaluated by three tutors, each of whom was responsible for a third of the students; this setting proved to be ineffective since insufficient evaluation criteria had been set, which resulted in the tutors being inconsistent. This setting also proved as extremely time‐consuming for the tutors. Further, the feedback showed that the students took longer time grasping the concept of CLIL without a “real‐ life” sample and considered the tasks to be too “isolating” in the sense that they did not have much opportunity to see their peers’ work. In 2010/2011, the CLIL e‐learning course was moved to DMDM as an optional course, whilst CLIL continued as a face‐to‐face course in its original setting at DELL. The contents of the e‐learning course were adapted based on the student feedback and also to reflect two new aspects: firstly, the majority of students were no longer language teachers, so the language‐teaching part of CLIL methodology had to be introduced and focused on; second, the now optative course had fewer credits (and thus had to have lower time demands). Furthermore, another need arose during the planning of the new e‐learning course: the students’ knowledge of English could no longer be taken for granted, and anxiety about using a foreign language in teaching practice was to be expected. In the end, topic 3 was dropped completely and topics 2, 5, and 9 were reorganized, the emphasis shifted notably to practical tasks. Shared forums and wikis were chosen instead of several individual tasks. Lesson observation remained in both modalities of the course. A survey was run at the end of the semester in both courses to evaluate the strengths and weaknesses of both approaches for future

355

Jarmila Novotná and Lenka Procházková Mathematics teachers (Novotná and Tejkalová 2010). The results of the survey led to implementing a blended approach at MDMD that would benefit from good practice of the two modalities.

2.1 The blended model The current blended model is built around the concept of learning community and it goes along with the national methodology handbook for e‐learning in higher education (Dlouhá et al. 2010). It features an introductory face‐to‐face seminar (2 hours) and a closing face‐to‐face session (4 hours). Between them, there are 5 units of e‐learning dedicated to CLIL theory of methodology. In the first seminar, the students (among other activities) participate in an opening CLIL activity in English (which most of them speak), to introduce CLIL methods and principles and to illustrate the use of language‐teaching methods in teaching Mathematics. Students also participate in a short CLIL activity in Spanish (which they do not speak) to show that language proficiency is not required for successful CLIL practice. The e‐learning sessions are dedicated to: 1. CLIL principles and methodology, sample CLIL videos on the web; 2. CLIL background: bilingualism and relevant language teaching theory and methodology; 3. CLIL stakeholders: sharing experience and good practices; 4. Scaffolding: strategies and principles, graphic organizers, language frames; 5: Lesson planning: commenting upon different lesson plans, preparing own lesson plan. In preparation for the closing session, the students comment on their peers’ lesson plans, suggesting improvements, highlighting strong aspects, looking for possible extensions. In the final session the students work in groups, sharing the previously‐read lesson plans; they also watch sample CLIL lessons, identify CLIL features and scaffolding principles, and in groups they suggest possible modalities. The closing session thus works as a “market of ideas” for the students to perfect their lesson plans. The second semester again takes the form of face‐to‐face seminar, where students plan and peer‐teach shorter CLIL episodes.

2.2 Aims of the course and CLIL principles in the blended model Table 1 (Aims and activities) presents the specific aims we identified for our introductory CLIL course and shows how they are covered in the e‐learning modules. Outputs are also listed; the two tutors of the course regularly check the forums to guide the discussion, correct misconceptions, answer questions and point students to further materials. The tutors also monitor the wikis. Table 1: Aims and activities in the e‐learning section aim get acquainted with CLIL approach, its specifics, models and methodology understand the benefits of CLIL accept the feasibility of Mathematics and English Integrated lesson in a Czech secondary school accept that language proficiency is not a requirement for a successful CLIL activity develop language awareness in Mathematics activities

recognize and use a variety of scaffolding strategies, focus on multimodal approach

activities (materials) in e‐learning theoretical lectures external links to CLIL‐dedicated websites links to CLIL promotional videos joining CLIL‐practitioners discussion group reading CLIL research data watching CLIL lessons online joining CLIL‐practitioners discussion group watching CLIL lessons online joining CLIL‐practitioners' discussion group theoretical lecture on language awareness and didactic adaptation of text webinar by a CLIL professional on levels of language in CLIL adapting foreign language activities for CLIL lessons external links to didactic and methodological sites sample lesson plans

356

output forum wiki

forum

text analysis of a specific task small‐group discussion

forum preparing a detailed lesson plan

Jarmila Novotná and Lenka Procházková aim understand the importance in planning for a CLIL lesson

activities (materials) in e‐learning sample lesson plans (examples of good practice), template for a generic CLIL lesson plan with detailed comments

include students in research get feedback on both CLIL approach and the course itself

online survey online survey

output forum preparing a detailed lesson plan preparing suggestions for colleagues’ lesson plans participating in online survey participating in online survey

Most of the above‐mentioned aims are directly observed also in the face‐to‐face sessions. The role of the tutors in these sessions is the desired role of the CLIL teacher: they act as the task‐assigners, monitors, task managers and later as facilitators/mediators of the discussions. Table 2 (Aims and activities in the face‐to‐face sessions) exemplifies the wider aims and details the activities in the opening and final sessions. Table 2: Aims and activities in the face‐to‐face sessions aim

activities in the first face‐to‐face session

get acquainted with CLIL approach, its specifics, models and methodology

watching introductory CLIL video, discussing pros and cons of integrated approach from different perspectives presenting sample CLIL activity in a language that the trainees speak (trainees in role of CLIL students), pointing out (listing) CLIL features presenting sample CLIL activity in a language that the trainees speak (trainees in role of CLIL students) presenting sample CLIL activity in a language that the trainees do NOT speak icebreaker activities (“language showers”) sample CLIL activity in a language that the trainees do NOT speak (trainees in role of CLIL students)

accept the feasibility of Mathematics and English Integrated lesson in a Czech secondary school

accept that language proficiency is not a requirement for a successful CLIL activity

recognize and use a variety of scaffolding strategies, focus on multimodal approach

understand the importance in planning for a CLIL lesson

activities in the closing face‐to‐ face session watching a CLIL video, identifying CLIL specifics and methods

sharing lesson plans

watching a video of a CLIL lesson, identifying scaffolding strategies employed students are driven into faulty group‐work settings to illustrate the risks; good practices are used ‐ and pointed out ‐ in further group‐ work sharing lesson plans preparing suggestions for sharing lesson plans

We present both authentic and adapted materials in the theoretical part of e‐learning to illustrate the benefits of sensible adaptation for CLIL classrooms. As for technology use, the course presents an e‐learning platform, uses an interactive board in its face‐to‐face sessions and presents platforms for creating surveys. As Jančařík and Jančaříková (2010) point out, a very important objective in the use of e‐learning courses in undergraduate teacher training is to "furnish students – future teachers with personal experience with the use of e‐learning courses in teaching". This is why the individual types of activities in the e‐learning (see Table 1 above) are subject of follow‐up discussions both in the forums within e‐learning and during the final seminar. The same is valid for the feedback questionnaire: we discuss the possibilities and limitations of the chosen platform for survey and also for use in classroom (testing or feedback tool).

3. Student feedback We collected students’ feedback in anonymous online questionnaires using the Google Forms platform. Scaled questions and unfinished utterances are employed to get both a comparable general overview and detailed answers and suggestions. The results regularly show a significant shift in students' attitude to CLIL (typically

357

Jarmila Novotná and Lenka Procházková starting from a fairly sceptical if not rejecting position) towards accepting the method. All of the students see the course as enrichment in their teaching repertoire (regardless whether they contemplate using L2 in teaching content subject in the future or not). All students preferred the blended model over a strictly e‐learning environment. The most frequently stated reasons were the following: the experience of a live CLIL activity convinced the trainees of feasibility of CLIL in a Czech class; getting to know the other students in person made the online discussions more natural; the face‐to‐face collective reflection on the lesson plans offered more insights and inspiration than an online debate would have, and allowed to refer to specific parts of the lesson plans swiftly and accurately. Erasmus students who took part in the course typically appreciated the e‐learning section of the course because of the language barrier with which they struggled during especially the final face‐to‐face seminar; however, they also underlined the experience of a live sample of a CLIL activity. Most students showed preference of the blended model over a traditional face‐to‐face class, acknowledging that the e‐learning part allowed them to pace their learning better and gave them enough time and resources to work with foreign‐language materials. They also welcomed contact with the in‐service CLIL teachers which would have been almost impossible without the e‐ learning part. In the first run of the blended model at DMDM the students’ feedback suggested that the e‐learning part of the course was challenging in terms of keeping up with the weekly workload. Consequently we decided to shift the e‐learning part of the course towards self‐paced learning, with all the activities being available from the beginning with only final deadlines being given. As expected, this setting was convenient to more students; however, the final feedback showed some students would have appreciated regular weekly tasks. We are inclined towards keeping the self‐paced model to teach students time‐management and illustrate yet another principle in teaching: unless the students are well trained, they tend to struggle with time and workload management.

4. Conclusions ‐ the future of the CLIL course The currently implemented blended learning model covers CLIL methodology on two levels: not only is it the subject of the course, it is also the method employed in the face‐to‐face seminars. The e‐learning sessions cover the crucial theoretical background of CLIL and allow students to share their ideas and work. The build‐up of the course leads the students to independent lesson‐planning which is later discussed in detail in collective reflections. There has been significant interest in the CLIL course among Erasmus students. On one hand, they contribute a multicultural aspect to the course and contribute to the multilingual reality of a CLIL class; on the other hand, their presence is a challenge since they are typically not proficient enough in Czech to follow a spontaneous discussion or a workshop. Several of the Czech teacher trainees have a rather limited command of English (some have only studied German or French throughout their education), which means that it is impossible to maintain an English‐only discourse to cater for the international students. The teacher needs to actively include the Erasmus students in the final face‐to‐face session, e.g. grouping them with more proficient Czech students. The students' feedback shows that all students preferred the blended model over a strictly e‐learning environment; most students also preferred it over traditional face‐to‐face class. This parallels the findings of Kupetz and Ziegelmeyer (2005). Watching a real CLIL lesson with a follow‐up reflection has been proven to be a very powerful tool in CLIL teacher training; even when it was dropped from the syllabus one semester, the majority of feedback called for a real‐life lesson sample. Live samples of CLIL activities turned out to be a strong motivational element. They are highly praised by the students as beneficial in terms of understanding the concept of CLIL, understanding its possibilities and limitations, and accepting the feasibility of CLIL classes at Czech schools. The trainees' feedback shows further interest above all in evaluation of CLIL lessons or activities; generally, it is one of the most discussed questions among CLIL practitioners, too (e.g. Marsh et al, 2010; Perez‐Cañado, 2012). In the future, we hope to offer a complex module on evaluation and assessment in CLIL as a follow‐up course to our current CLIL programme, available also to in‐service teachers.

358

Jarmila Novotná and Lenka Procházková Since 2012/2013, the CLIL course at DMDM has become a compulsory subject for all Mathematics students. This brings a new challenge: so far, we have dealt with students who chose the course knowing that the vehicular language is English. Now, all Mathematics teacher trainees are enrolled, some of them having studied only German or French. Thanks to automated translation tools and abundance of materials online, the e‐learning part can be easily adapted to cater to the multilingual audience. In the face‐to‐face sessions, more attention needs to be paid to checking comprehension: on the other hand, even fluent English speakers benefit from the slower‐paced approach, as it simulates real school environment with diverse language levels.

Acknowledgements This article was supported by Quality in education project SVV 267‐402 Kvalita ve vzdělávání a ve výchově

References Dlouhá, J., Braniš, M., Dlouhý, J., Jančaříková, K., Jančařík, A., and Šauer, P. (2010) Metodika tvorby textů v otevřeném internetovém prostoru pro vysokoškolské vzdělávání formou e‐learningu, Centrum pro otázky životního prostředí Univerzity Karlovy v Praze, Praha. Jančařík, A., and Jančaříková, K. (2010) “Wiki Tools in the Preparation and Support of e‐Learning Courses”, Electronic Journal of e‐Learning, Vol 8, No 2, pp 123‐132. Kupetz, R. and Ziegenmeyer, B. (2005) “Blended learning in a teacher training course: Integrated interactive e‐learning and contact learning”. ReCALL, 17, pp 179‐196. Marsh, D. and Lange, G. (1999) Implementing Content and Language Integrated Learning, Continuing Education Centre/TIE‐ CLIL, Finland. Marsh, D., Mehisto, P., Wolff, D., and Frigols Martín, M.J. (2010) The European Framework for CLIL Teacher Education., [online], http://clil‐cd.ecml.at/Portals/24/flashfiles/index3.html. Milne, E.D., Llinares A., and Morton, T. (2010) "CLIL across Contexts: A Scaffolding Framework for CLIL Teacher Education." Current Research on CLIL, Vol 3, No 12, pp 12‐20. Novotná, J., Hadj‐Moussová, Z., and Hofmannová, M. (2001). “Teacher training for CLIL – competences of a CLIL teacher”, in M. Hejný, J. Novotná (Eds.), Proceedings SEMT 01, Charles University in Prague, Faculty of Education. Praha, pp 122‐126. th Novotná, J., and Hofmannová, M. (2005). “Teacher training for content and language integrated learning”, in 15 ICMI Study Conference: The Professional Education and Development of Teachers of Mathematics, [online], http://www.weizmann.ac.il/G‐math/ICMI/log_in.html. Novotná, J., and Moraová, H. (2005) “Cultural and linguistic problems of the use of authentic textbooks when teaching mathematics in a foreign language”, ZDM, Vol 37, No. 2, pp 109‐115. Novotná, J., and Tejkalová, L. (2010). “Two settings for training CLIL teachers of mathematics., in th Pinto, M., Kawasaki T. (Eds.), Proceedings of the 34 Conference of the International Group for the Psychology of Mathematics Education, Belo Horizonte, Brazil: PME, Vol. 4,. p 368. Pérez‐Cañado, M. L. (2012) “CLIL research in Europe: past, present, and future”. International Journal of Bilingual Education and Bilingualism, 15(3), pp 315‐341. Quartapelle, F. (Ed.) (2012) Assessment and Evaluation in CLIL, Ibis, Como – Pavia.

359

[Teaching Desktop] Video Conferencing in a Collaborative and Problem Based Setting Rikke Ørngreen1 and Per Mouritzen2 1 Research Lab: IT and Learning Design, Aalborg University, Denmark 2 IT and Service, Aalborg University, Denmark rior@learning.aau.dk per@its.aau.dk Abstract: This paper presents experiences from teaching video conferencing for learning and collaboration, and discusses the challenges and potentials of applying a collaborative and problem‐based learning (PBL) pedagogy. The research is an action research study, and we as researchers, educational planners, teachers and assistant teachers wanted to find ways in the design for learning that enables the learners to acquire knowledge about the theories, models and concepts of the subject, as well as hands‐on competencies in a learning‐by‐doing manner. In particular we address the area of desktop video conferences. We studied 3 subsequent years of a master program module on video conferencing, and the changes it has undergone. The participants work in groups and each group has the task of designing a short one hour (45min) educational design of their own choice. The students have to try out and evaluate their educational design and their role as teachers in this video conference setting. The students reflect on their experiences and designs in a blog and the group collaboratively hands in a reflection paper online. Both blog posts and reflection papers needs to relate to the literature of the module. Our analysis shows that the students experiment with various pedagogical situations, and that during the process of design, teaching, and reflection they acquire experiences at both a concrete specific and a general abstract level. The desktop video conference system creates challenges, with technical issues of delay and sound problems, where different embodiment and space‐design influence the learning process. However, we also find that the PBL‐setup inspires the students to apply theory into practice and to reflect on their own practice, furthermore the collaborative approach support the feeling of trust which is crucial when building on a competence as personal as teaching. Keywords: desktop video conferencing, competence development, problem‐based learning (PBL), problem‐oriented project‐pedagogy (POPP)

1. Introduction Video conferencing is a strategic choice for many organizations, as administrators hope for cost‐effective teaching and collaboration over distances. This leaves many teachers and professionals with new challenges. With this research we focus on stepping further than traditional online lecturing and webinar formats, to collaboration, problem‐ and project‐orientation, and further develop aspects of the concept tele‐presence and ‐experience for this setting. Consequently, this paper is cross‐disciplinary involving the fields: video‐ conferencing for learning, PBL methods, and collaborative learning. Video‐conferencing for learning: The purpose of the module under investigation is to give students insight and experience with the use of video conferencing for learning and collaboration in both educational and work contexts. The primary video conference technologies applied are various desktop video conference systems. The learning goal is that students will be able to critically assess the needs and requirements for tools for different types of activities, with emphasis on knowledge of interaction, communication, and cooperation. (Kear et al. 2012) report on a number of positive effects of using a web conference system, but they also discover that tutors find it more difficult to improvise in web conference systems compared to traditional face‐ to‐face teaching. As researchers in the field we have observed and participated in numerous sessions that used a less interactive form, with the teacher(s) taking the dominant role. This can be very suitable in some situations, but given the learning objectives of this module, we needed another pedagogical approach of collaboration and exploration, and as the Master in ICT and Learning (hereafter called MIL) is a PBL inspired education, this was a natural place to start. PBL and collaborative approaches: (de Graaff & Kolmos 2007) outlines several PBL variances and give a historical outset for the project orientation of PBL on which the MIL education is based. Project orientation can vary, depending on whether the students are given a task, a predefined problem to work on, or the students themselves define the problem area, the definition of the problem to work on, as well as the method and theories to apply. (Savin‐Baden 2007)) identifies a number of ways to work with PBL that shows its great variety: from working on solutions to a problem in a somewhat teacher defined way to whole educations that

360

Rikke Ørngreen and Per Mouritzen apply problem orientation. Aalborg University identifies itself as a PBL‐university, applying the recognized Aalborg Model (Kolmos et al. 2006) that integrates the PBL‐thinking with what is known as problem‐oriented project‐pedagogy (POPP) (Kolmos et al. 2006). The project‐orientation at Aalborg University does not necessarily entail collaboration. The projects can be individual, however in POPP the overall paradigm is social constructivism, focusing on knowledge sharing among peers. Within this line of PBL and POPP, new thoughts has emerged on how to combine these in digital supported learning processes (Dirckinck‐Holmfeld 2009). The ability to improvise during a teaching session is much needed in a PBL setting, where control is given to the participants: “Participant control implies that the institution or the teacher cannot fully guide or control the learning process. Problem formulation is always a leap in the dark. It is the subsequent theoretical and empirical enquiry that really displays the results of the collaborative learning situation.” (Dirckinck‐Holmfeld 2009, p. 4). Given the amount of literature on how difficult it is for some teachers to foster interaction and give up control, our research focus has been to develop a learning design that enables us as teachers to facilitate learning in a PBL/POPP manner, allowing for the necessary interactive but also unforeseeable events (se for example Hedestig & Kaptelinin 2005, Majid et al 2006). Within this setting, we have investigated how does space and layout of digital space, constitute and influence the learning experience? In the following, we present the MIL‐case and the applied educational design, then we give a brief summary of the students designs for learning and move on to the analysis of the digital spaces and the learning experiences.

2. The MIL case and the educational design MIL students are mature adults. The majority have years of experience from practice, and although they all have an interest in learning and technology, they come from very different backgrounds and from both the private and the public sector. It is a part‐time study over 2 years, and the pedagogy is a blended mode where the students meet face‐to‐face 3 times a year for 2½ days seminars, and the remaining teaching and collaboration between students take place through online activities. This video conference module is a 5‐ECTS‐module developed and taught by Rikke Ørngreen and Marianne Georgsen (in 2010 and 2011) and named: the didactic of video conferencing. It was slightly redesigned by Rikke Ørngreen (in 2012) and re‐named: video conferencing for learning and collaboration with teacher assistance from Per Mouritzen. The students’ works in groups and their assignment is to design, run, and evaluate an approximate 1 hour educational design either with their fellow students (role‐playing the target group of the educational design) or with the actual target group. In case of the latter, they present the design and their experiences with the real target group to their fellow students. The domain area of the design, the learning objective and even the video conference system used is left open for the students to decide. The educational design is illustrated in figure1 and can be outlined as follows: Each year, the module has a start‐up activity at a face‐to‐face seminar (approximate 5 hours), and then it continues online for 6½ week. The online activities varies from asynchrone discussion boards, blogs, and project activities in shared documents, to synchrone presentations, discussions, and supervision using video conference tools as Adobe Connect (hereafter called Connect), Skype and Google+/Hangout. In between the teacher involved activities, the student’s work on their PBL activities in collaboration. There is a mix of video conference use, with and without teachers, in small groups and in plenary sessions. At the face‐to‐face seminar (session 1) the students are introduced to the video conference field (concepts and themes) and discussions on challenges and opportunities commence. Then follows a discussion of the possible problem‐areas and projects the participants would like to explore and design during the module. This dialog has as the point of departure the students’ work practice and their aspirations. Practical issues are coordinated, such as establishing the work plan, establishing themes and groups, and making sure everyone has access to Connect and the blog. The evaluation of 2011 showed that the primary focus on desktop video conferencing left some participants with the unmet need to experience and talk about campus‐to‐campus conferencing. This was included in the

361

Rikke Ørngreen and Per Mouritzen first session at the face‐to‐face seminar in 2012. At the seminar, we reserved two classrooms, in close vicinity to each other and experimented with the system in a sandbox‐approach by simulating distance.

Figure 1: The structure of the module on video conferencing In the PBL group process, the students meet with the teachers in a video conference: First in a plenary session to acquiring experience with video conferences for collaboration ranging from different design of virtual rooms to social fun with a distance‐sing‐along (session 2); Then in a supervision session to get fed forward to their design and progress (session 3); Finally, at the day of trying out or demonstrating experiences from their learning design (session 4). At all times, the students have the opportunity to raise issues, share information or ask questions in the First Class forum (the LMS of the MIL education). The teachers are online once a day and students can therefore expect an answer to a question within 24hours during workdays. Due to the students work‐hours, all video conference sessions takes place in the evening. The assessment criteria of the module are through satisfactory and active participation. During the process of making their learning design, students acquire experiences with and the students reflect on their experiences and designs in a blog. The individual blog posts should relate to the modules literature and in 2012 each student should write a minimum of 3 blog‐posts, within 42 hours of the session 2‐4. The teacher participated with mediating blog‐posts that highlighted issues put forward by the students, and generalized or discussed them further by relating to previous experiences and existing literature. The module is completed with each group writing a reflection paper, which they receive written feedback and feed forward to. These papers relate to the practical experiences from the module and should once more apply the literature from the module.

3. The students educational designs The students came up with a broad spectrum of educational designs from paper‐folding challenges with the learning goal to engage the body in video conferences for children, over discussion of marketing strategies, to role‐playing in the military. Each group was allowed to choose an online system of their choice that worked for their design, but all groups ended up using Connect. The main reason was properly due to easy access, as the program was provided free by the university in a no‐limited addition (google hangout only allows for 7 multiple videostreams), and it was also introduced during session 2 (figure 1). Other reasons seemed to be due to the many functions Connect provide. For example, they can design different templates before a session, enabling the designed space to change while the session runs; they can assign different roles; and they can apply break‐

362

Rikke Ørngreen and Per Mouritzen out rooms, which are virtual meeting rooms, where participants can be sent to by the “owners” of the session (in this case the group of students). To give a better understanding of our analysis and of how the hands‐on‐ experiences relates to the theories‐in‐use, we present a few exemplary designs from the most recent module (2012/2013). One group explored how to design an online meeting room to give the participants a sense of presence, being mentally in the same place, even though they physically are situated in different spaces (Dourish 2006). They wanted to examine what happens when using different modalities in an online meeting. To examine this, they tried three different settings with the participants, and documented the process by participating as observants in each setting. The students met in a plenary Connect room for introductions by the planning‐group. This Connect room was owned and designed by the student‐group and had break‐out rooms. After the introduction the students were divided into groups of maximum 4, and sent to the break‐out rooms. The groups did not know that they did not have the same communication‐channels/tools available for their assignment. The assignment was to “design a meeting room”. In one break‐out room they were only allowed to use audio, in another break‐out room the where allowed to use audio and video, and the last break‐out room the used audio, video and a program called floorplanner, which they used by the desktop sharing function in connect. After this exercise that was monitored by a group member in each break‐out room, the session ended with a discussion in plenary (see figure 2), where the different starting points were revealed. The group in charge wrote about their immediate reflections and experiences in the blog, as well as on their different roles during the session, as observants, discussion facilitators and technical responsible persons. The final paper that was handed in also included a very thorough analysis of the empirical data collected during the process.

Figure 2: Plenary discussion in the “design a meeting room”‐assignment Another group used a similar setup, but with a difference in objective and in technological setup. The focus was communication and negotiation, specifically to agree on important events from 2012 in a short time‐slot. They wanted to examine the difference in communication when people have different modalities available: one group with access to video and sound, the other only with access to sound. As the above described group, this group had a plenary Connect room for introduction and discussion. However, for the group work, the participants were asked to log‐in to another meeting‐room in Connect. This is the only technical way to record the session, for example when you want the video available for later reviewing and analysis. Break‐out room activities cannot be recorded as everyone is still technically in the same Connect session. As the previous group, the student‐group could have chosen to attend the break‐out‐rooms as observants. However, they had made a design where they did not want to interrupt or take over control of the activity. Having people to move

363

Rikke Ørngreen and Per Mouritzen from one Connect session to another turned out to be a little challenging, in such a short timeframe, but nevertheless it worked in the end. As mentioned by many in the reflection blog‐post afterwards, this challenge was probably intensified by the fact that unlike all the other groups, this group did not have a technical facilitator appointed. Although, all the participants found their way, either by helping each other or themselves, this difference was felt. A third and final example is a group that wanted to explore what could be identified as engaging factors when using video conferencing with school children at app. 10‐12 years old, when learning Danish grammar. They tried to carry out their learning design with the actual target‐group. However, they ran into technical difficulties as this was done in a private‐household, using several computers on one Wi‐Fi connection. They experienced so much delay that the learning process could not be executed. The group documented this and in their 45min presentation and then they asked us as participants to try out the planned design, both using break‐out‐rooms and gamification elements. The gamification elements used the facilities of the Connect room (the whiteboard etc.), but also engaged in low‐key technologies, as when the participants were asked to write on paper and just hold the result up to the webcam. This idea of using fast methods for distributing results by holding objects to the webcam is something we in our research has used several times and had discussed in our teaching. Often we find teachers use a lot of time on distributing pictures from the mobiles to the class folders and then to the shared desktop or smart board. Holding objects, the mobile screen etc. up to the webcam is a low‐fidelity solution, but works well and focusses more on the learning objectives.

4. The teachers condensed analysis As researchers and teachers we have during and after each year of the module evaluated on the learning process and the students experiences as a whole. The MIL program performs standard evaluations which has been very positive indeed, and confirms our educational design (in particular this last year – 2012/2013), with the exception of two qualitative remarks about how the module, though engaging, required a big effort to carry out. Our analysis hereunder investigates the actual situations as they unfolded, a much richer empirical outset than the formal evaluations. We focus on the use and layout of digital space, and how PBL applied in video conference settings constitute and influence the learning experience. Tele‐experience It is notable how everyone feels that one hour of teaching using videoconference demands more energy than normal classroom teaching. This was also found in the (Kear et al. 2012) study, where tutors found themselves “fatigued” and “wiped out”. They argue that the reason is the complexity of the interface (many types of media and streams of communication in play). In our situation, we found teachers and students alike experienced this, perhaps due to the PBL‐setting, where students have active roles. Also, in desktop video conferences, everyone perform a focusing / a zooming of the senses. This hyper‐zoom is necessary, as some of the senses that are normally helps us in judging a situation via the body and the peripheral sense of the room, cannot be used to the same degree via video. Another perspective is that we tend to use more “effective” time together. For example, there is less chit‐chat, less moving about in groups etc. There is also an extra cognitive load caused by awareness of oneself based on the image shown in the video conference. It is not that we as participants want to “look good from the right angles”, on the contrary there seems to be a relatively relaxed atmosphere, with people participating in the most fascinating angles: from the “cam” on an iPad lying flat on the desk and students leaning over the desk, to strange back‐lit settings. We found it is often just a “being made aware of own presence” that we are not accustomed to, and even if this is just for a brief second or less at a time, it makes a difference. This corresponds with findings of people performing worse in job‐situations of video conferencing, where they can see themselves (Wegge 2006). Although we found participants did not perform worse, just differently and more self‐aware, it may add to the cognitive strain and energy use in a video conference session (when the video stream / webcam is turned on and not hidden by documents). As mentioned the students could choose platform and often chose Skype/Google+ hangout for supervision, and Connect for teaching their educational design. It could be that they are familiar with theses first two platforms, and feel comfortable with them. Trying other platforms means leaving their comfort zones (Penteado 2001), and they want to play‐it‐safe when having supervision. Perhaps the students experience as

364

Rikke Ørngreen and Per Mouritzen we do that for a small number of participants, these freeware often works better, and a more intuitive. The sound is better, and Connect does not mirror the image, and for many it seems unnatural to look at themselves in a non‐mirrored image. As such, the design of the different platforms, the designed space, constitutes frames for the design of communication. Another point of “un‐natural” vs. natural frame of communication is the concept of gaze. A lot of papers deal with the direction of the eyes; that one cannot look directly into the eyes of the participants (by looking into the webcam) and at the same time watch the participants look back at you (by looking at the computer screen). In the simulation of a two‐campus solution at the seminar day, we discussed gaze direction: How the teacher may direct his/her eyes towards the camera to establish a more engaging tele‐experience. It may seem “un‐natural” to look in the direction of the ceiling (because the camera was mounted higher than the participants), but it supports in establishing a common tele‐experience and an element of trust. For PBL‐ pedagogies the element of trust is vital and looking into a webcam in desktop video conferences may interrupt visual cues of trust. This was discussed in the blog and also got some attention in our synchronous dialogs. The student mentioned that when the teacher sometimes in the dialogue looks smiling at me, into the camera, it seems like cheating. We argue that people today are accustomed to the skype/hangout way of communication that a culture of how this is done has been established. Either people look at the one they talk to (i.e. the computer screen) or at everything and nothing. People do not look directly at the camera and smile. When doing so, it could feel as a try to establish a connection that is not there, i.e. cheating. The unnatural way of speaking indirectly to people’s faces in video conferences, may have become the natural tele‐experience way, where we use our bodily senses of the emotions displayed by the other to connect with each other. This is a new way of using our bodily senses in digital spaces, and because of its newness, it may add to the cognitive load discussed above. Collaboration on technological pedagogical features and technical problems Common questions the first time the module ran were ”can you hear me?” and “do I have sound?” Even though there were sound problems during the last module virtually no one asked this question. We believe one reason was the technical walk‐through at the first online session (session 2). The introduction had moved the students from their comfort zone into a risk zone (Pentado 2001), meaning the students felt safe enough in the Connect space to take some risks. We also saw that the students supported each other in their groups. Some groups used dedicated roles, where many applied a technical facilitator during their 45min session. Hedestig and Kaptelinin talks about the technical facilitator as a role that do more than technical support. The technician acts as an assistant teacher, meeting the students before and after the session and therefore knows them at a semi‐personal level, and assists the teacher by directing his attention to students with questions. The technician can also predict if the equipment has or is going to have a breakdown, e.g. if the picture begins to drop‐out. (Hedestig & Kaptelinin 2005). Technological problems are of course present, and audio/visual delays and need for explicit turn‐taking increase as the number of participant increase (see also (Ruhleder & Jordan 2001)), in particular when the number of participant rise from around 10 to 15‐20. In a state‐of‐the‐art and literature review of desktop video conferencing, (Smith 2004) concludes that this is “most suitable for collaborations with single individuals or small groups of up to three four people” (p. 23). We agree that this lowers the complexity, and that true collaboration in a PBL‐setting is difficult with many people online, but we also find that the number of people participating can be bigger depending on the learning design. Although this does require a set of “knowing what to do” and “plan B’s”, when things do not go as planned. Several groups used the break‐out‐room feature. This is an excellent tool for process management of group‐ sessions, however, as some discovered it sometimes management as in the sense of control. The planning‐ group had a control button to send people to their groups, rearrange them, send messages to, etc. This led to people felt they lost control. Some got frustrated: “I was in the middle of a sentence and then wupti…” was an experience we heard often, when the participating students were called back to the plenary session. In a traditional group work, students will often rise from the chairs, move in and out of the classroom, and they will themselves decide whether to end in a sentence or carry‐on. As such the video conference features influence the pedagogical‐space.

365

Rikke Ørngreen and Per Mouritzen Recordings as reflection tools Some groups used the recording tool in Connect, to be able to analyse the learning design afterwards. This method is supported by Thurairajah et al (2011), whose findings points to the benefits of using synchronous conferencing in distance learning, but also finds that the recordings and asynchronous use are of great value for the students. Desktop video conferences are easy to record and share. Rattleff and Holm (2009) describes that teachers’ worry about their organizations re‐use of recordings and the students worry about who can see them in the future. However, their studies also show that students use recordings in support of their learning (stopping, pausing and reflecting). These types of studies all address recordings of teachers’ lectures. In PBL/POPP it is a process‐perspective and recordings work as reflection tools. This is confirmed by Tripp & Rich (2012) where video‐based‐analysis of own practice was catalysts for changing own practice. Also, the teacher role is dramatically different. In PBL it is a facilitating role, where the subjects and the actions taking place cannot be 100% a priori defined, this makes the teachers dilemmas with being made obsolete due to recordings of their lectures less problematic. The learning and communication process We have observed many different utterings between students, and between students and teachers. When Majid et al (2006) studied the interaction patterns in a video conference learning situation, they identified dimensions of interactions and categories of exchange: teacher talk to students, student talk to teacher or students initiate and direct other students) (Majid et al 2006). A number of similar interaction and exchange forms took place in this module. We also found that a lot of the interaction is non‐verbal, also in video conferences. We saw how the shared digital materials provide the possibility to collaboratively “build” something, and how the building of arguments, of keywords, of drawings are a vital part of supporting the communication and the PBL‐process in non‐verbal ways. This is an added‐value experience, which our traditional face‐to‐face interaction‐form does not cater for. Our analysis shows that the PBL‐setting allows for a very lively dialog, and the students use many of the functions while learning about video conferences. It was, however, more difficult for the student to explore the pedagogical spectra in their own design, in particular the first two times we ran the module. Our analysis shows that for some students it is difficult to move beyond the more traditional teaching formats: a lecture or instruction, then a group assignment, and a plenary session. As mentioned two alterations affected this: The brief technical introduction/walk‐through to allow a sense of familiarity with the possibilities, and secondly, we tried to frame the PBL‐module as a sandbox, as a chance to explore and play with technology. This playfulness was further established by having a sing‐a‐long. At first we introduced this as a heartwarming farewell at the end of the module. But for the last run of the module, we used it as an ice breaker. We played a traditional well known (to almost everyone) song in a karaoke form and tried to have people sing‐a‐long from their various distributed positions (figure 3). A completely impossible task, due to delay of sound, but also very funny and relaxing.

5. Discussion and interpretations The form of not only talking about but also doing video conferencing (in a learn‐by‐doing thinking) seems to work very well. Compared to other modules in other educations with which we have experience the literature comes well into play (it should be duly noted, that we as teachers had explicitly framed that theory and the literature of the module should be used in the blogs and in the final paper). Besides technical issues, such as technical interruptions we found that the students in the collaborative and group processes are able to carry out and learn from their sessions, while exploring many different PBL‐settings and different pedagogics. The analysis also shows that there are interesting aspects of how we experience each other via video conference. Some functions as break‐out‐rooms and the setup of various windows provide different spaces for us to interact with each other. As other researchers confirms, the element of self‐awareness plays a significant role when carrying‐out video conferences. We found desktop video conferences adds another dimension to self‐awareness, and the concept of trust in eye contact / gaze seem to change, as our everyday familiarity with the skype/hangout facilities has shown a new way of communicating. In the beginning these new forms of teleexperience and telepresence might add to the cognitive strain on the participants.

366

Rikke Ørngreen and Per Mouritzen

Figure 3: Screengrap from sing‐a‐long exercise As teachers, one of the challenges has turned out to be how to support students in moving their focus beyond technical problems (like log‐in, delay and using advanced features in connect), to enable students to discuss video‐mediated communication and learning on a more academic level of a master module. On the other hand, technical issues do play a significant role; as such it cannot be ignored. This entails that the roles of teachers, facilitators and students should be discussed, when we know such issues will occur. We find that giving the students some basic technical skills in the beginning minimize problems underway, particularly if they know how to solve them themselves (adjusting the Connect microphone and camera settings). Similarly, the PBL/POPP setting was framed in an atmosphere that gave room for sandbox‐thinking and playfulness. The very different learning designs that the student groups worked with meant that everyone got experience with and reflected on areas that we as teachers would not have been able to include. As facilitators, we were able to recognize which aspects would be relevant to bring forward in the discussions, but in general the students themselves identified relevant aspects. Thus in cooperation we found and discussed subjects from the literature, but also subjects that are not visible in the literature, as the issue of gaze. As such, we find that the PBL format not only work as research‐based‐education but also as an education‐generating‐research in a mutual learning process.

6. Conclusion in relation to teaching this module In this paper we have shown a possible scenario for teaching desktop videoconferencing through problem‐ based‐learning approaches, and the roles and learning strategies applied. Through this work we identified aspects important for the learning experience, the design of the video conference space and tele‐experiece. We also found that to some extend the video conference learning and teaching mirrors students existing practices and thus provides an opportunity for the students to reflect on own practices, whether in video conference setting or not. Our roles as teachers are to create a frame, where students get an array of practical experiences, to initiate reflection at several levels, and to expand collective knowledge and knowledge in the field collaboratively. We have created this frame by using a number of IT tools (Connect, blogs…), but find that the educational design (the schedule of events and facilitated activities) in particular contributes to the students learning. In this PBL/POPP setting the frame was given (to develop and try‐out a learning design), but how to reach the learning design, what to put in it, which topic and empirical data to use for the paper were based on students’ own choices. As teachers we try to further progression by participating in the ongoing discussion, in the plenary video conference events, through group supervision and onwards by commenting on the papers written.

367

Rikke Ørngreen and Per Mouritzen

Acknowledgements First and foremost we thank the students participating in this module for the three years described and of course also Marianne Georgsen, who were in the teacher‐team for the first two years.

References Graaff, E & Kolmos, A 2007, 'Management of change: Implementation of problem‐based learning in engineering', in Ed Graaff & K Anette (eds), Management of change, Implementation of problem‐Based and Project‐Based learning in engineering, Sense, Rotterdam, pp. 1‐8. Dirckinck‐Holmfeld 2009, 'Innovation of Problem Based Learning through ICT: Linking Local and Global Experiences', International Journal of Education and Development using ICT, vol. 5, no. 1. Dourish, P 2006, 'Re‐space‐ing place: "place" and "space" ten years on', paper presented to Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, Banff, Alberta, Canada. Hedestig, U & Kaptelinin, V 2005, 'Facilitator's roles in a videoconference learning environment', Information Systems Frontiers, vol. 7, no. 1, pp. 71‐83. Kear, K, Chetwynd, F, Williams, J & Donelan, H 2012, 'Web conferencing for synchronous onlnie tutorials: Perspectives of tutors using a new medium, Computers & Education', vol. 38, no. 3, pp. 953‐63. Kolmos, A, Fink, FK & Krogh, L 2006, The Aalborg PBL model ‐ Progress, Diversity and Challenges, Aalborg University Press, Aalborg. Majid, O, Rahman, ZA, Ghani, NA, Guan, SK, Idrus, RM & Atan, H 2006, 'The Video Conferencing Learning Environment in Distance Education: A Study of the Interaction Pattern', paper presented to Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies. Penteado, MG 2001, 'Computer‐based learning enviroment: risks and uncertainties for teachers', Ways of knowing, vol. 1, no. 2. Roberts, R 2009, 'Video Conferencing in Distance Learning: A New Zealand Schools’ Perspective', Journal of distance learning, vol. 13, pp. 91‐107. Ruhleder, K & Jordan, B 2001, 'Co‐constructing non‐mutual realities: Delay‐generated trouble in distributed interaction', Computer supported cooperative work, Springer, pp. 113‐38. Savin‐Baden, M 2007, 'Challenging perspectives and models of problem based learning', in Management of change, Implementation of problem‐Based and project‐Based learning in engineering, Sense, Rotterdam/Tapei, pp. 9‐29. Smith, JD 2004, Effective Use of Desktop Videoconferencing in Teacher Education and Professional Development, With Reference to Strategies for Adult Basic Education, National Center on Adult Literacy, University of Pennsylvania. Thurairajah, N, Williams, A & Mcadam, B 2011, 'Using synchronous web conferencing to enhance situated distance learner experience in a built environment context , in: Education in a Changing Environment', paper presented to (ECE) 6th International Conference : Creativity and Engagement in Higher Education, The University of Salford, Greater Manchester, UK, <http://usir.salford.ac.uk/17019/>. Tripp, TR & Rich, PJ 2012, 'The influence of video analysis on the process of teacher change', Teaching and teacher education, vol. 28, no. 5, pp. 728 ‐ 39. Wegge, J 2006, 'Communication via videoconference: emotional and cognitive consequences of affective personality dispositions, seeing one's own picture and disturbing events', Hum.‐Comput. Interact., vol. 21, no. 3, pp. 273‐318.

368

Challenging Pre‐Service Teachers’ on Collaborative Authoring of Learning Designs in a Blended Learning Context Kyparisia Papanikolaou and Evangelia Gouli School of Technological and Pedagogical Education, Athens, Greece kpapanikolaou@aspete.gr lilag@di.uoa.gr Abstract: In this paper we explore the potential of collaboration in a pre‐service teacher training course on technology enhanced learning as well as the skills that trainees cultivate when working with learning design environments, developing Web 2.0 objects, and collaborating using specific pedagogical tools. Technological Pedagogical Content Knowledge (TPACK) framework is used as the basis for designing the curriculum and content of the course with a constructivist perspective aiming to cultivate various types of knowledge such as technology content knowledge (TCK), technological pedagogical knowledge (TPK), and technological pedagogical content knowledge (TPACK). Trainees worked initially individually to get familiar with technological and pedagogical tools, and then in groups in order to develop a learning design. Groups were mainly interdisciplinary with mixed ability on using technology since they were formulated either randomly or according to the individual characteristics of the trainees such as personality traits and other psychological variables (e.g. self‐efficacy, anxiety and attitudes). Preliminary results are discussed focusing on the way that trainees perceived collaboration with peers as well as on how capable they became in using several technological and pedagogical tools and integrating them in technology enhanced course designs. More specifically, trainees seem to appreciate collaboration in groups and many of them seem to face interdisciplinary collaboration as an opportunity that promotes discourse and ideas exchange. The difficulties they faced in collaborating with the members of their group are focused on the different time schedules, ways of thinking, working and behaving, not being familiar with group work and the variety of disciplines involved in the group. Also, trainees seem to acknowledge the technological and pedagogical tools proposed as useful for designing activities and technology enhanced courses but they characterize the process followed as time consuming. Trainees’ learning designs provide evidence about the capabilities they developed in designing courses that sufficiently integrate technological tools under the principles of a learner‐centered contemporary pedagogy. Keywords: teacher training, learning design, collaboration, TPACK, ICT in education

1. Introduction Teacher training on Information and Communication Technology (ICT) usually focuses on the technical skills or background knowledge of ICT rather than the pedagogic use. However both are considered critical in preparing teachers capable of integrating technology into a real educational context (Mishra and Koehler 2006). A main challenge in designing a teacher training course that promotes teachers to elaborate on the learning opportunities that ICT can provide to students and connect it to their context in school, is to make it happen in a constructive manner promoting reflection, collaboration, and discourse (Hawkes and Romiszowski 2001; Papanikolaou and Grigoriadou 2009). The Technological Pedagogical Content Knowledge (TPACK) framework was firstly proposed by Mishra and Koehler (2006) focusing on the connections of three critical parameters relating to technology integration in classroom settings, namely content, pedagogy and technology. TPACK is widely used for improving teachers’ knowledge and skills to support productive technology integration in their classroom (So and Kim 2009). Although it also promotes research in pre‐service teachers’ education and in‐service teacher professional development (Koehler and Mishra 2009; Jimoyiannis 2010; Niess 2005), the implementation of the framework in teacher education has been limited. In this paper we present and discuss a case study of an interdisciplinary pre‐service teacher training course on educational technology. Our work with pre‐service teachers falls into the tradition of involving teachers in authentic problem solving with technology. We mainly focus on how the TPACK framework can be used as the basis for designing the curriculum/content of a course and evaluating learning designs of technology enhanced courses authored by interdisciplinary groups of pre‐service teachers, aiming to contribute to the research of TPACK implementation in teacher education. We also explore the potential of collaboration in a teacher training context as well as the skills that they cultivate when working with learning design environments, developing Web 2.0 objects, and collaborating using specific pedagogical tools, aiming to contribute to the collaboration scripts’ research line.

369

Kyparisia Papanikolaou and Evangelia Gouli

2. Design rational of an interdisciplinary pre‐service teacher training course 2.1 TPACK for teacher training TPACK as a framework can support understanding of teachers’ knowledge required for effective technology integration as it focuses on connections, interactional, affordances and constraints between and among technology, content and pedagogy, emphasizing their complex interplay. TPACK has inspired the design of professional development courses usually focusing on specific disciplines or subjects such as social studies (Hammond and Manfra 2009), physics (Jimoyiannis 2010), science and mathematics (Niess 2005), inclusive educational practice (Marino, Sameshima, and Beecher 2009), integration of Web technology into educational pedagogical practice (Lee and Tsai, 2009). Although existing research data offer evidence that the TPACK model when used for designing courses improves teachers’ knowledge and skills increasing the possibilities for productive technology integration in their classroom, its use in teacher education and specially for pre‐service teachers has been limited with no clear method for its implementation or evaluation (Cox 2008). In the particular teacher training course that prepares pre‐service teachers of various disciplines in integrating technology in classroom, the types of knowledge that are cultivated focus on the interaction of technology with content and pedagogy assuming that trainees have sufficient pedagogical content knowledge, i.e. understanding of how particular aspects of their subject matter are organized, adapted, and represented for instruction. Thus, trainees work mainly with three types of learning activities:

TPK (technological pedagogical knowledge) activities that raise questions about the appropriate matching of technologies with various pedagogical approaches,

TCK (technological content knowledge) activities that raise questions about how difficult concepts or misunderstandings might be faced using technology,

TPACK (technological pedagogical content knowledge) activities that raise questions about how a new technology might best serve specific learning outcomes going beyond all three types of knowledge.

2.2 Collaborative work in a learning design basis The findings of several studies show that student teachers appreciate the learning value of pre‐service experiences with collaborative learning (Veenman et al. 2002).They value the opportunity to explain and listen to other class members’ explanations of key course concepts, as an opportunity to become better acquainted than usual with their peers. In the particular course TPACK is cultivated as trainees collaborate in the design of an interdisciplinary course merging learning outcomes from their subject areas (if possible), and authoring it using a learning design environment. In training courses that have as a target group, trainees from a variety of disciplines, we have to balance among discipline‐oriented and interdisciplinary goals. In our case, trainees initially work on tasks asking them to focus on their own discipline and then they work in groups in order to develop interdisciplinary courses combining learning outcomes from various disciplines, if this is possible. More specifically, teachers working in groups undertake a project of designing a technology enhanced educational scenario as a sequence of activities based on the pedagogical framework proposed. They have also to develop learning objects using various Web 2.0 technologies and locate useful web resources and tools. This scenario should be authored in LAMS (Learning Activity Management System ‐ http://www.lamsinternational.com/). It should also embed the learning objects they have developed. Finally, they have to present and justify the decisions they made concerning both (a) the pedagogical decisions (scope, learning outcomes, type of knowledge processes that each activity cultivates, didactic techniques used, types of activities developed for the students) and (b) the use of specific technology – how the particular learning objects were developed and how they support the learning outcomes. Trainees’ collaborative work was organised as a collaboration script (Dillenbourg and Hong 2008) promoting them to work at first individually in order to familiarize with various technologies, web resources and the pedagogical framework proposed, and reflect on their added value for the particular disciplines. Then they worked in groups developing a learning design and authoring it to LAMS. LAMS was considered appropriate for the particular target group and task, as a visual authoring environment allowing the creation of sequences of

370

Kyparisia Papanikolaou and Evangelia Gouli learning activities based on both content and collaboration offering a range of informative, reflective, assessment, and collaborative tools.

2.3 Curriculum In this Section the course curriculum is considered according to the various types of knowledge that cultivates based on TPACK (see Table 1). Tasks were designed for the various topics, implemented either in face to face (F2F) workshops or as individual/collaborative assignments running online. In F2F workshops, technological and pedagogical knowledge were mainly cultivated, whilst TPK, TCK and TPACK through online assignments. In particular, Technological Knowledge (TK) involves the skills required to operate particular technologies. A main design decision for a training course is the technologies that will be included. In our case, the technological knowledge refers to

web resources for finding and sharing educational and multimedia content, evaluation criteria for web resources and copyright issues,

Web 2.0 tools. Through face to face courses teachers worked with various categories of Web 2.0 tools such as tools for graphical representations, digital storytelling, and assessment,

authoring tools for individual and/or collaborative development of learning designs.

Content Knowledge (CK) refers to content covered in school within the trainees' field. Moreover, it is quite important that trainees understand the nature of knowledge and inquiry in different fields. Trainees are usually of different disciplines, considered competent in their own field. The course aims at cultivating the link among the scientific knowledge with the subjects taught in school as well as promoting a culture of interdisciplinary collaboration. Pedagogical Knowledge (PK) is about specific pedagogical approaches including teaching/didactic techniques or methods, characteristics of target student group including individual needs and preferences, and strategies for assessing student understanding. The Learning by Design framework (LbyD) which is based on the conceptualization of ‘New Learning’ (Kalantzis and Cope 2012) was used as the pedagogical background for designing activities aiming to enable communication among teachers during the co‐construction of learning designs enhanced with technology. The LbyD framework uses eight ‘knowledge processes’ (i.e. types of activities) (Kalantzis and Cope 2012): (i) Experiencing the known, (ii) Experiencing the new, (iii) Conceptualizing by naming, (iv) Conceptualizing with theory, (v) Analyzing functionally, (vi) Analyzing critically, (vii) Applying appropriately, and (viii) Applying creatively. Cultivating the range of knowledge processes through a course is intended to foster higher order thinking skills. The learning activities that trainees design for their students should also involve teaching/didactic techniques, tools and resources, interactions and roles of those participated. A typology of activities was also proposed in order to give trainees ideas about various types of interactions that may take place during an activity such as assimilative, information handling, adaptive, communicative, productive, experiential (Laurillard 2002). Trainees were involved in online individual/collaborative tasks (assignments) in order to mainly cultivate TPK, TCK and TPACK (see Table 1). The individual activities were starting in class during F2F workshops and should be completed before a particular deadline and submitted on the Moodle environment. Trainees were proposed to work in groups in order to design educational scenarios facing designs as sequences of activities from teachers’ perspective concerned with designing, planning, orchestrating, and supporting learning activities that involve roles for learners, teaching/didactic techniques, and web resources (TPK/TPACK). Following Conole (2007) trainees were asked to design an educational scenario constituted of three components: context, pedagogy, and tasks. The context includes the subject matter (in our case trainees were promoted to select interdisciplinary topics), the learning outcomes that were mapped to Bloom’s taxonomy as well as the skills of the 21st century that the scenario aims at cultivating (http://www.p21.org/overview/skills‐ framework).

371

Kyparisia Papanikolaou and Evangelia Gouli Table 1: Curriculum of the technology enhanced learning course organised in F2F workshops and online assignments that correspond to various types of knowledge according to TPACK Topic Educational & multimedia resources on the Internet

Web 2.0 tools Pedagogical framework, Teaching /Didactic techniques Learning Design tools for Course Authoring

Tasks F2F workshop on educational & multimedia resources on the Internet Individual Assignments: Find and evaluate web resources for their discipline and share with peers through the class forum with comments on the usefulness of such resources in their educational practice. F2F workshops on the use of various categories of Web 2.0 tools Individual Assignments: Develop artefacts using specific tools such as prezi, glogster, pixton, video authoring, and comment on the usefulness of the specific tools for their subject F2F workshop on designing activities based on the Learning by Design framework using appropriate teaching/didactic techniques

Knowledge TK TCK

F2F workshop on LAMS as an authoring environment for technology enhanced learning courses Collaborative Assignment: Develop and author a course design for LAMS based on the LbyD framework working in groups

TK TCK

TPK/TPACK

3. Methodology The case study performed in the context of a technology enhanced learning course offered in the one‐year postgraduate certificate in education of the Higher School of Pedagogical and Technological Education (ASPETE) for graduates of a variety of disciplines. Two classes participated in the study, Class A including 40 trainees of various disciplines such as computer science, marketing/advertisement, graphical design, business administration in tourism, and Class B including 45 trainees of other disciplines such as sociology, law and political science, economic and financial sciences, forestry and natural environment. Moodle was used for administration purposes, content delivery, enabling also communication and collaboration beyond the F2F meetings/workshops. Through the course trainees worked individually and in groups (13 groups of Class A and 13 groups of Class B were formulated having 3 or 4 members). The trainees were organized in groups randomly or taking into account their individual characteristics such as personality traits (based on the five‐factor personality model) and other psychological variables such as self‐efficacy, anxiety and attitudes (5 groups of Class A and 5 groups of Class B were formed based on this ’protocol’). This group formulation approach resulted to interdisciplinary groups usually with mixed ability on using technology. At the end trainees completed a questionnaire evaluating the experience of collaboration as well as of the design framework proposed. In this study we aim at elaborating on the following research questions:

RQ1: How collaborative learning design is perceived by pre‐service teachers?

RQ2: How pre‐service teachers use technological and pedagogical tools when developing learning designs?

3.1 Data collection and analysis The data collected include the questionnaires that the trainees completed at the end of the semester as well as the group products developed. The trainees’ answers to each question of the questionnaire were analyzed and similar answers were grouped. Table 2 presents the trainees’ answers to five questions asking them to reflect on the collaborative work and the way they collaborated with their peers (Questions 1‐4). The last question aims at evaluating the pedagogical tools offered to support trainees in designing pedagogical sound courses and communicating their ideas to each other. As far as the group products are concerned, we analyzed the learning designs of indicative groups from each class (i.e. the groups that formulated on the basis of the personal characteristics of their members; 5 groups from Class A and 5 Groups from Class B), authored in LAMS along with their documentation. Particularly we explored how the particular groups integrated specific technological tools in learning activities, in which types of activities, with which didactic techniques, and which knowledge processes they aimed at cultivating.

372

Kyparisia Papanikolaou and Evangelia Gouli

3.2 Results 3.2.1 RQ1: How collaborative learning design is perceived by pre‐service teachers? As we can see in Table 2, Question 1, trainees seem to appreciate collaboration in groups. They underline the development of various types of skills and creativity (Answer 1.1). Many of them seem to face interdisciplinary collaboration as an opportunity that promotes discourse and ideas exchange (Answer 1.2) as well as friendship and synthesis of ideas. Just a small percentage (5%) of Class B didn’t seem to appreciate working with people they hadn’t chosen (Answer 1.6). Some of the students believe that there is no difference of the particular type of group compared to others (Answer 1.5) whilst others changed positively their attitude towards collaboration with people they don’t know (Answer 1.4). Based on trainees’ answers to Question 2 of Table 2 about the difficulties they faced in collaborating with the members of their group, they consider as main issues: the different time schedules, different ways of thinking/working/behaving, not being familiar with group work, the variety of disciplines involved in the group. However, most trainees seem to be positive towards group work (Answer 2.1). Next, Question 3 concentrates on the particular phases of the work that collaboration proved helpful. Based on trainees’ answers, they seem to appreciate group work and especially for elaborating on theoretical issues and the pedagogical framework. In this line, the next question, Question 4 asking what they would change in a next collaboration, most trainees focus on the process of collaboration referring to management issues, roles and responsibilities, coordination, work planning. However, a small percentage would prefer to select peers for working on collaborative tasks (Answer 4.5). As far as the design framework and tools proposed (Question 5), trainees seem to acknowledge the specific pedagogical tools as useful for designing activities and courses but also for promoting discourse and communication in the context of a collaborative authoring task (Answer 5.1), for supporting teachers in designing pedagogical sound technology enhanced courses (Answer 5.2), for enabling monitoring (Answer 5.3). Most didn’t seem to have faced problems in using the proposed tools although some of them characterize this process as time consuming (Answer 5.5). Table 2: Most popular answers of students to the questionnaire evaluating their perceptions about collaboration and the design framework proposed Teachers’ perceptions on collaborative work and framework proposed Class A Class B Question1: Usefulness of collaborative work in groups 1.1 Development of communicative/social/collaborative skills (adaptation, interaction, 31% 42% synchronization) – development of creativity 1.2 Knowledge level: Interdisciplinary collaboration, ideas and knowledge exchange, new 39% 36% way of learning / thinking 1.3 Cultivated friendship, exploration and integration of common interests 19% 30% 1.4 Overcome stereotype – resistance/fear to such type of collaboration and its results 6% 14% (specially of communicative problems) 1.5 No difference compared to other types of collaboration (e.g. with people you have 3% 14% selected) 1.6 Enhanced attitude about not working with people you don’t know ‐ 5% 1.7 No usefulness 8% 5% Question 2: Difficulties in collaborating with the members of the group (since you didn’t know them) 2.1 None 28% 48% 2.2 Different ways of thinking, working, behaving 6% 14% 2.3 Different time schedules – lack of common free time 17% 2% 2.4 Delays due to not being familiar with group work and each other 11% 9% 2.5 Difficulties in finding the subject of the course due to the different disciplines of the 14% 7% group members 2.6 Work coordination, available time for working and communicating ‐ 16% Question 3: Steps that collaboration was necessary 3.1 All 28% 47% 3.2 Designing the educational scenario 30% 23% 3.3 Designing activities for specific knowledge processes 42% 25% 3.4 Designing activities for LAMS 17% 25%

373

Kyparisia Papanikolaou and Evangelia Gouli Teachers’ perceptions on collaborative work and framework proposed Class A Class B 3.5 Collecting resources – information 8% ‐ 3.6 Evaluation of final product and corrections 6% 14% 3.7 Partitioning of work for designing activities and justifying design decisions 8% 7% I3.8 deas exchange – brainstorming for the educational scenario, activities, design decisions 8% 20% etc. Question 4: What would you change in the way you worked and the collaboration model you adopted in a next collaboration with the same group? 4.1 None 47% 43% 4.2 Better work partitioning – undertake roles with specific responsibilities 3% 9% 4.3 Better group management in organizing activities, time scheduling for collaboration, 14% 30% place for meetings – in time engagement of group members 4.4 More frequent F2F meetings 17% ‐ 4.5 I would prefer to choose my partners of the group 8% 7% Question 5: Usefulness of design framework proposed 5.1 Innovative way of working promoting collaboration and discourse and mutual 14% ‐ understanding of the task 5.2 Useful for designing activities that cover a range of knowledge processes & skills and promote students’ engagement and new methods of teaching. Useful guide for organizing 61% 70% the scenario and teaching based on learning outcomes. Improves course design from the teacher perspective 5.3 Useful for setting learning outcomes goals and monitoring their attainment during ‐ 7% teaching 5.4 Interesting – Satisfactory ‐ Flexible ‐ Practical – Usable 14% 7% 5.5 Too much detail – time consuming process of design 6% 18%

3.2.2 RQ2: How pre‐service teachers use technological and pedagogical tools when developing learning designs? Table 3 presents data about the way that various technological tools were used under the proposed pedagogical framework combined with specific types of activities, didactic techniques and knowledge processes. An example of a learning design in LAMS as viewed by the author during the authoring process is illustrated in Figure 1. Trainees of both classes produced designs as sequences of activities that exploit most the informative (e.g. noticeboard, image gallery, tasklist), the reflective (e.g. notebook, question‐and‐answer, survey, mind map) and the collaborative (e.g. forum, chat) tools of LAMS. Trainees of Class A have used all the categories of LAMS tools in five out of six types of activities (i.e. assimilative, information handling, communicative, productive and experiencing) while they haven’t proposed adaptation activities – this worths to be further investigated. Trainees of Class B have used all the types of tools offered by LAMS in the six types of activities. The majority of LAMS tools from all the categories were exploited mostly in productive, experiencing and communicative activities. In particular trainees exploited all the different categories of LAMS tools in order to produce learning designs that comprised of activities which focused on asking students to "do something" undertaking control over their learning as active learners and collaborating with their classmates. This indicates that trainees follow in their designs the principles of the learner‐centered contemporary pedagogy and exploit the technological tools in a sufficient way under the specific principles. Table 3: Data of indicative groups of Class A and Class B reflecting the use of technological tools and resources combined with pedagogy in the final group product CLASS A Tools

LAM S

Web

Inform ative Reflecti ve Assess ment Collabo rative Hotpot

Types of activities

Didactic Techniques A r

W s

Knowledge processes E C A A E x o n p v 4 5 2 4

2 1

3 2 2

A I A C P E E P D T s H d o r x s r s 5 2 3 3 2 5 4 2 2

Q A

C M

T s

E G 3

2 2

1 3 2

1 2 1

1 3

5 2 1

2 5 1

2 2

2 2 1

374

Kyparisia Papanikolaou and Evangelia Gouli CLASS A

2.0

atoes Prezi, Glogste r

Types of activities

1 2

Didactic Techniques

Knowledge processes

Tools & Text, images, video, editors, search engines, data bases, assessment tools virtual worlds, forums, mind Reso mapping software, interactive boards urces CLASS B Types of activities Didactic Techniques Knowledge processes Tools A I A C P E E P D T Q I I T E A C W A B K S V E C A A s H d o r x s r s A n R s G f S s r r x o n p Inform 5 1 1 1 1 2 2 1 1 1 1 1 1 1 1 5 2 1 1 ative Reflecti 4 2 3 3 2 1 1 1 1 2 2 3 ve LAM S Assess 1 1 4 4 1 1 3 1 4 1 2 1 2 2 ment Collabo 2 1 3 2 1 2 4 1 2 1 2 2 1 3 2 3 1 rative Wiki 1 1 1 1 Web Comic 1 1 1 2.0 Glogste 1 1 1 r Tools Text, images, video, search engines, data bases, forums,, assessment tools, virtual worlds, chat & Reso urces

E v 2 4

Types of activities: As=Assimilative, IH=information handling, Ad=adaptive, Co=Communicative, Pr=Productive, Ex=Experiencing Techniques: Es=Essay, Pr=Presentation, Ds=Discussion, T=Task, QA=Question‐Answer, CM=Concept Mapping, In=Interview, IR=Individual Reference, Ts=Test, EG=Educational Game, Af=Artifact, CS=Case Study, Ws= Web Search, Ar=Argumentation, Br=Brainstorming, K=Keywords, S=Simulation, V=Voting Knowledge Processes: Ex=Experiencing the known/new, Co=Conceptualizing by naming/with theory, An=Analyzing functionally/critically, Ap=Applying appropriately/creatively, Evaluating Trainees of Class A used three Web 2.0 tools in four types of activities, while trainees of Class B used three Web 2.0 tools (2 different from Class A) in just two types of activities. Glogster (creation of interactive posters) is the most popular one, while most of the Web 2.0 tools were used in productive activities. The number and the variety of the Web 2.0 tools included in their learning designs are not considered sufficient as trainees worked with various categories of Web 2.0 tools in F2F workshops and this needs further investigation. Trainees involved a variety of didactic techniques in their designs, twelve didactic techniques for Class A and seventeen for Class B. Question‐Answer, discussion, task and presentation were in favor of most designs while most didactic techniques were applied by exploiting the informative and communicative tools of LAMS. Lastly, all the four categories of LAMS tools were used to support the eight knowledge processes. Specially, the informative, reflective and communicative tools of LAMS were used mostly while it is quite interesting that assessment tools of LAMS were used from Class B not only for evaluation purposes but also for analyzing and applying. The learning designs include mainly experiencing, analyzing and applying activities asking students to recall and reflect on their own familiar experiences, observe something new, analyze connections and functions, evaluate their own and other people's perspectives and apply new learning. Finally we observed that most groups incorporated in their designs, activities that mainly cultivate one knowledge process instead

375

Kyparisia Papanikolaou and Evangelia Gouli of a combination. Just one group incorporated activities that can be used across more than one knowledge processes.

Figure 1: A learning design authored in LAMS as a sequence of activities

4. In conclusion In this paper we discussed the design specifications of a training course for pre‐service teachers of various disciplines including technological as well as pedagogical aspects viewing teacher training as an authentic process involving participants in learning design activities. We also discussed how collaborative authoring of learning designs may be organized in order to prepare trainees for designing their own courses and collaborate with peers of different disciplines. Considering the difficulties to comprehend and apply TPACK in various educational settings (Cox, 2008; Lee and Tsai, 2009), this study adds to our knowledge of organizing interdisciplinary teacher training courses indicating activities for cultivating various types of knowledge included in TPACK such as TCK, TPK, TPACK, as well as collaborative learning, and promoting positive attitudes over collaboration with peers of different disciplines. Analysis of group products focused on how technology was combined with pedagogical tools. This was the first step for using TPACK for analyzing pre‐teachers’ learning designs and evaluating types of knowledge they have developed, mainly the dimensions of TPK, TCK and TPACK. Our future plans include the investigation of specific indicators of trainees’ knowledge in the group products based on the TPACK framework. The results of this study will also guide changes in the course curriculum as well as the interactions promoted through the course.

5. Acknowledgements We wish to thank all the trainees who participated in this study. We gratefully acknowledge the assistance of K. Asimakopoulou for her contribution to the evaluation of trainees’ group products. Τhe research “Design, Implementation and Evaluation of Blended Learning Scenarios in a Teacher Training Context Accommodating their Individual Psychological Characteristics (BleSTePsy)” is implemented through the Operational Program “Education and Lifelong Learning” and is co‐financed by the European Union (European Social Fund) and Greek national funds.

376

Kyparisia Papanikolaou and Evangelia Gouli

References Conole, G. (2007) Describing learning activities, In: H. Beetham and R. Sharpe (eds.), Rethinking Pedagogy for a Digital Age. Designing for 21st Century Learning, 1st Edition, London and NY: Routledge, pp 84. Cox, S. (2008) A conceptual analysis of technological pedagogical content knowledge, Doctoral dissertation, BrighamYoung University, http://contentdm.lib.byu.edu/cdm/ref/collection/ETD/id/1486 [Last access 30/8/2013]. Dillenbourg, P. and Hong, F. (2008) The mechanics of CSCL macro scripts, Computer‐Supported Collaborative Learning, Vol 3, pp 5–23. Hammond, T. C. and Manfra, M. M. (2009). Giving, prompting, making: aligning technology and pedagogy within TPACK for social studies instruction, Contemporary Issues in Technology and Teacher Education,Vol 9, No 2, 1pp 60–185 Hawkes, M. and Romiszowski, A. (2001) Examining the Reflective Outcomes of Asynchronous Computer‐Mediated Communication on Inservice Teacher Development, Journal of Technology and Teacher Education, Vol 9, No 2, pp 283‐306 Jimoyiannis A. (2010) Designing and implementing an integrated technological pedagogical science knowledge framework for science teachers’ professional development, Computers & Education, Vol 55, pp 1259–1269 Kalantzis, M. and Cope, B. (2012) New Learning: Elements of a Science of Education (2nd ed.), Cambridge, UK: Cambridge University Press. Koehler, M. J. and Mishra, P. (2009) What is technological pedagogical content knowledge?, Contemporary Issues in Technology and Teacher Education, Vol 9, No 1, pp 60–70. Laurillard, D. (2002) Rethinking university teaching: A conversational framework for the effective use of learning technologies (2nd ed.), London: RoutledgeFalmer. Lee, M.‐H. and Tsai, C.‐C. (2009) Exploring teachers’ perceived self efficacy and technological pedagogical content knowledge with respect to educational use of the World Wide Web, Instructional Science, Vol 38, No 1, 2010, pp 1‐21 Marino, M. T., Sameshima, P. and Beecher, C. C. (2009) Enhancing TPACK with assistive technology: promoting inclusive practices in preservice teacher education, Contemporary Issues in Technology and Teacher Education, Vol 9, No 2, pp 186–207. Mishra, P. and Koehler, M. J. (2006) Technological pedagogical content knowledge: A framework for integrating technology in teacher knowledge, Teachers College Record, Vol 108, No 6, pp 1017–1054. Niess, M. L. (2005) Preparing teachers to teach science and mathematics with technology: developing a technology pedagogical content knowledge. Teaching and Teacher Education, Vol 21, pp 509‐523. Papanikolaou, K. and Grigoriadou, M. (2009) Co‐authoring personalised educational content: teachers' perspectives, AIED workshop 'Enabling Creative Learning Design: How HCI, User Modelling and Human Factors Help', held in conjunction with the 14th International Conference on Artificial Intelligence in Education (AIED 2009), Brighton, UK. So, H.‐J. and Kim, B. (2009) Learning about problem based learning: student teachers integrating technology, pedagogy and content knowledge, Australasian Journal of Educational Technology, Vol 25, No 1, pp 101–116. Veenman, S., Benthum, N., Dolly., B., Dieren, J. and Kemp., N. (2002) Cooperative learning and Teacher Education, Teacher Education Vol 18, No 1, pp 87‐103.

377

Technology‐Enhanced‐Learning and Student‐Centeredness in a Foreign Language Military Class – a Case Study Maria‐Magdalena Popescu, Ruxandra Buluc, Luiza‐Maria Costea and Speranza Tomescu Carol I National Defense University, Bucharest, Romania mpopescu03@gmail.com ruxandra007@yahoo.com luizamariacostea@gmail.com speranzat@yahoo.com Abstract: We currently live in a competitive security environment, and this is one strong reason why we must prevail in the competitive learning environment as well. Those involved in military learning, be they educators or students, must build a “learning model by leveraging technology without sacrificing standards”, so that we can provide credible, rigorous, and relevant training for the ever‐developing forces – both soldiers and leaders. Therefore, establishing a continuum of learning for those who are both digital immigrants and digital natives in the same class is a challenging task for educators who must adopt technology‐enhanced‐learning (TEL) as their second nature in order to meet the requirements adult learners have, especially in foreign language courses. Having to come to grips with issues like time factor, deployment, interoperability and scarce funding, teaching English for the military means not only terminology, language proficiency and realistic knowledge transfer but also flexibility, student‐centeredness and ubiquitous learning environments. In this respect, the present paper looks at a case study unfolded in Carol I National Defense University in Bucharest, Romania, with a view to answering the following general question: How does TEL, and eLearning per se, answer the military students’ training needs when it comes to foreign language acquisition, so that knowledge transfer is performed at such a rate and in such a manner that immediate applicability guides the learning process? To this end, the following goals were set: (1) Analysing if TEL meets the requirements of andragogy in the military environment; (2) Researching to what extent knowledge transfer and skill aquisition have been performed by the subjects in the case study; (3) Investigating by what means e‐assessment can be performed in this ESP military environment, what limitations it exhibits, and if and how they can be overcome. The study was performed on a segment of 75 students aged 25 to 50, attending intensive or non‐ intensive courses. A number of four educators were involved, aged between 30 and 45. The findings of all these questions will form the basis of the present paper, and enforce the idea that, in the current military context, only TEL can meet the needs and demands of students for whom technology is a part of their daily lives. The conclusions could be of use for any educators involved in teaching various subjects to specialized adult students where ubiquitous learning is centerstage. Keywords: technology‐enhanced‐learning, andragogy, e‐assessment, military education, foreign languages, TEL ESP educators, student‐centeredness

1. General background Multinational operations in the Balkans, Afghanistan and Iraq have constantly highlighted frequent malfunctions in military’s interoperable skills when the deployed pesonnel wass immersed in and faced with different cultures. Moreover, various lessons learned have shown that language proficiency and understanding of foreign cultures are vital factors for the full spectrum of operations. While current education and training programs are helping soldiers meet some needs, the gap between culture and foreign language capability requires building cohesion, mastering language function skills necessary in leading operations, collaborating and eventually being interoperable, in one word. To improve or literally build on the aforementioned skills is a complex task as a military student is ″people centric”, fully capable and skilled in high technology operations; he is ″trained best to accomplish goals and objectives and perform duties and responsibilities in cross‐cultural contexts with host nations participants” (Raybourn et al., 2005). In this context, a military’s education, training and experiences influence the learner’s development of knowledge, skills and attributes. However, when it comes to learning a language, one needs to know how to use it in specific cultural contexts in order to fully operate and maximise goal effectiveness (Army Culture and Foreign Language Strategy, 2009), not just master the vocabulary and gramatical structures. Hence, in order to be prepared for real time operations, our students need to be exposed to a training that is intercultural (knowledge, beliefs, attitudes, skills and behaviors), linguistic, and personal (self‐confidence, self‐ awareness, self‐reliance). Moreover, considering that decision‐making is at the centre of all military operations and that our learners needs to perform this in a multi‐cultural environment, the decision‐making techniques need to be taught experientially, and technology‐enhanced learning (TEL) appears to be the optimum solution to shape both knowledge transfer and skill building or reinforcement, irrespective of the hard or soft skills to

378

Maria‐Magdalena Popescu et al. be taught. Consequently, we proposed an experiment to answer several questions regarding the compared efficiency of face‐to‐face learning versus TEL in the military environment.

2. TEL and andragogy within the military environment The shift from the traditional learning environment to the cyberspace environment has triggered the need for a closer look at how adult students best experience learning in the 21st century. Teachers in general, and teachers of foreign languages in particular, debate whether the adult learner in cyberspace looks to the educator for help with language exclusively or with content as well, whether technology enhances knowledge transfer or it just boosts the pragmatism of the already assimilated information. Looking at studies on andragogy (Cranton, 2000, Knowles, 1984) as educators teaching adults, students’ characteristics as learners should be considered.. The adult students have concrete and immediate learning goals that lead them to be voluntarily involved in the learning situation as self‐reliant learners. Their learning process capitalizes on their own life experiences whereas they transform their previous knowledge by learning, even though they may be reluctant to change their values, opinions or behaviors at certain times. These learners nowadays need to survive and thrive in a global knowledge economy and apply skills and competencies to new situations in an ever‐changing, complex world (Kuit & Fell, 2010). For teaching them in the 21st century, with 21st century skills, andragogy is no longer fully sufficient, and a more self‐directed and self‐determined approach is needed, one in which the learner reflects upon what is learned and how it is learned, and in which educators teach learners how to teach themselves (Kamenetz, 2010)‐ this is what heutagogy stands for. To this end, new technologies have created ”a need for considering new pedagogical approaches, in the light of recent rapid development in new teaching methods, learning resources, and digital media” (Wheeler, 2011).That is one of the reasons why Technology‐Enhanced‐Learning is the answer. With its basis in andragogy, heutagogy further extends the andragogical approach and can be understood as a continuum of the aforementioned approach. In andragogy, curriculum, questions, discussions, and assessment are designed by the instructor according to the learner needs; in heutagogy, the learner sets the learning course, designing and developing the map of learning, from curriculum to assessment (Hase, 2009). Heutagogy emphasizes development of capabilities in addition to competencies. In heutagogy, the learning process is self‐ regulated (process of taking control of and evaluating one's own learning and behavior); it can be used to describe learning that is guided by metacognition (thinking about one's thinking), strategic action (planning, monitoring, and evaluating personal progress against a standard), and motivation to learn (Winne & Perry, 2000; Boekaerts & Corno, 2005). Self‐regulated learners are cognizant of their academic strengths and weaknesses, and they have a repertoire of strategies they appropriately apply to tackle the day‐to‐day challenges of academic tasks. These learners hold incremental beliefs about intelligence (as opposed to fixed views of intelligence) and attribute their successes or failures to factors (e.g. effort expended on a task, effective use of strategies) within their control (Dweck, 2002). Finally, students who are self‐regulated learners believe that opportunities to take on challenging tasks, practice their learning, develop a deep understanding of subject matter, and their effort will lead to academic success (Winne & Perry, 2000). All the above mentioned are attributes of an effective learning environment perfectly tailored to the military learner. Using TEL for the military students especially in relation to foreign language learning and intercultural competence reinforcement is answering the military student’s learning style, as military students are responsive to a teaching that gives immediate applicability, to an information that is presented in a synthetic and pragmatic way, to motion and interaction; military students are visual and kinesthetic learners more than audio ones.TEL does offer a self‐regulated learning, by tackling both meta‐cogniton in doing the tasks on the computer and strategic action in performing tasks embedded in virtual reality activities such as strategic games or simulations, but also by developing motivation to learn in witnessing immediate evaluation, strengths and weaknesses, wins and loosses. To endorse all of the above, our experiment unfolded within the Carol I National Defence University in 2012 – 2013 academic year and it was designed for our students refreshing functional language and shaping cultural awareness in the pre‐deployment eLearning course; to this end they attended a learning management system (LMS) – integrated game for pre‐deployment training, meant to be a guided practice sequel to the theoretical input. This game is not yet SCORM based and it does not benefit from all the LMS advantages, but delivered this way it gives the instructors the possibility of tracking the learning progress and evaluating the level of

379

Maria‐Magdalena Popescu et al.

knowledge acquisition. AFGHANISTAN‐cultural awareness pre‐deployment course ‐ a self‐paced learning course which can be improved in effectiveness if coached by educators ‐ collaborative activities and other stimulating assessment methods, comes after a theoretical module, as an application and better transfer retention, combined with assessment of the knowledge already gained on the basics of cultural awareness. Being self‐paced we cannot speak about a given time frame, as the user chooses his own deadlines to end, pause, research for information , self‐correct and bring the game to completion. The course we are speaking about is tailored for adult training. Consequently, the learning process is autonomous and self‐directed. Students select a certain part they identify with, by envisaging the deployment assignment they will have, and they are free to direct themselves and connect learning to their own experience; they can be either liaison or negotiator, they can assume the role of civil‐military cooperation officer or patrolling, so they become autonomous in selecting a context they want to experience according to the short term goal they have in a module sequence, and react accordingly to the input they have received; they can either re‐iterate the goal or drop it, based on their own self‐evaluation, and also on the conflict escalator index present on the screen. The course is also goal‐oriented and this element is made clear from the beginning. Upon enrolling in this course, adults know what goal they want to attain and therefore, appreciate an educational program that is organized and has clearly‐defined elements. Thus, the course embedded in the LMS covers the adult learning principles and also answers the TEL instruments – in being distributed to students, the AFGHANISTAN‐cultural awareness pre‐deployment course uses distance, both synchronous and asynchronous teaching and learning, as well as collaboration and cooperation‐ for the assessment phase.

3. Are knowledge transfer and skill acquisition performed better with TEL or face‐to‐face interaction ? Computer mediated communication (CMC) as Mike Levy (2006) put it, often offers authentic learning, especially if simulations, games or interactive activities are embedded for teaching via this means. However, there are many restraints imposed by the environment as well as many demands for real‐time processing of information which lead to a focus on meaning to the detriment of form. In order to perform the task at its best and gain winning points, the learner has to prove deep comprehension of the message. This entails the fact that students, pressured by the need to answer requests, make observations, collaborate in real‐time, often ignore grammatical principles for the sake of transmitting a message. It is true that a variety of communication strategies (von der Emde, Schneider and Kötter 2001:219) are used in order to make up for the lack of structural accuracy. Moreover, the focus of the students’ attempts to negotiate linguistic impediments falls on vocabulary items, better said on paraphrasing, replacing, or simply explaining the meaning of words that are not readily available in their vocabularies. These findings are analogous with those of Fernandez‐Garcia and Martinez‐Arbelaiz (2002:290) and Blake (2006:232) who conclude that clarifying lexical incongruities is paramount when negotiating meaning in CMC, but this is of utmost importance if the students play against a virtual peer which does not allow for too much interpretation, based on the already written codes of the program. When dyads with humans on both sides are formed, then form comes second, meaning being often rendered by paraphrasing or other lexical hints at hand. Yet even so, TEL provides authentic learning in that it immerses the learner into real‐life like environments, with immediate applicability and real‐time assesment. As Dewey stated “Authentic learning’s focus on real‐world problems brings a critical dimension to teaching and learning. Students see these real‐world problems as relevant in their day‐to‐day lives. Real‐world problems provide the motivation for students to learn through their interaction with concepts, people, materials, and environments.” (Dewey, quoted in Mathur & Murray; 240) What Kirschner et al. call “core skills or transferable skills” (2004:3) cannot be developed in a traditional classroom setting. They require a more flexible approach to learning and teaching, and the advent of technology coupled with the shift of focus onto the student are more likely to produce these desired results. Students cooperate and collaborate thus making knowledge transfer (by explaining to others), and reinforce the existing information, build skills and become more cognizant of the cognition they have been exposed to. Seen as authentic learning suppliers, cooperative and collaborative learning, both similar and different make partners split the work, solve subtasks individually and then assemble the partial results into the final output in cooperation, while collaboration makes them do the work together.” (Dillenbourg 1999:8)

380

Maria‐Magdalena Popescu et al. From this perspective, collaboration is a much better method for foreign languages because it presupposes active communication between students at all stages of a project, not solely at the beginning and the end. Consequently, their language as well as communicative skills become centerstage while also handling a real‐ life task. More collaboration time can be alloted in real face‐to‐face classes, in role‐plays and survival scenarios unfolded within the intensive onsite class, rather than in virtual environments. In virtual or TEL environments, cooperation is most widely used ‐ each student performs his/her own sub‐task and then they meet as a group to join the parts together in a continuum. This was also the case in the pre‐deployment course where‐ based on each and every mission students chose to experience virtually‐ all learners produced an After Action Report and provided the ″commanding officer ” with the results of the missions accomplished. On the other hand, in onsite learning, students collaborated throughout the survival scenario they were submitted to, and there was a continuum in communication as they had to solve the tasks collaborativelly, synergetically, synchronously, to reach consensus. Needless to say, there are, pedagogically speaking, types of learning activities that can be better performed online and activities that are designed to be performed in a face‐to‐face environment only.

Figure 1: Interaction scheme in a lecture‐based activity compared to the collaborative, game‐based activity As seen from the representation in figure 1, the cooperation ( right hand side) and collaboration( left hand side) bring along different classroom management and are tailored to different learning environments.What is to be mentioned is that the constant interaction ( written, listened, but also spoken in some cases) that the online environment presupposes can encourage students to move beyond basic linguistic acquisition into the realm of productive use of a language so as to mirror their metacognitive skills,as Kirschner et al. (2004:4) explain, highlighting the advantages of online and TEL learning over real classroom. However, Kirschner et al. (2004:11‐12) point out the fact that these learning and interaction processes in the online environment should be monitored closely because there are several possible outcomes: developing the target skill, developing part of the skill, or developing the skill and other unpredicted elements. In our case, the cultural awareness online non‐intensive course allowed for more team‐building and other affective skills, cultural awareness triggering inter‐personal and intra‐personal skills complemented by the metacognitive ones, trained and developed by the way the course was designed ‐ with sequences alternating the training stages with self‐reflection and input access, in a double loop learning.

4. Observations about students’ work 4.1 Language aspects To prove TEL effectiveness was higher in teaching language and culture than in face‐to‐face traditional classroom, our case study shows how technology enhanced learning ‐ LMS embedded cultural awareness module better helps in building soft skills in adult learners along with language acquisition. To this end, we have taken 40 students in a non‐intensive online course and 35 ones in an intensive face‐to‐face course. The online ones took the cultural awareness module having English as a working language, while the face‐to‐face ones took lessons on military terminology, language and functions, focused on military discourse, leadership skills and specific terminology. Both courses covered 30‐hour span and focused on collaboration and cooperation which play a major role in the study of foreign languages, as the reason for learning another language is primarily to communicate, to engage in social interactions making use of the acquired linguistic knowledge. Along the course we have made some interesting observations regarding the students’ acquisition of linguistic and cultural awareness competencies. In a foreign language environment, where so many minute details must be taken into account in order to fully develop one’s linguistic abilities, it is of extreme importance to monitor the students’ online progress.

381

Maria‐Magdalena Popescu et al. In the case of our military students, it has been noticed that, indeed, the focus has been on lexical terms both in the online and in the face‐to‐face environment, the difference being that in the latter case, the students, possibly pressured by the presence of the educator, also made attempts to check their grammar. In the online environment, complex grammar was either avoided or misused and the educator needed to intervene repeatedly to get the students correct their errors. Formulaic language was also clearly evident in our CMC context. Should we mention that Fernandez‐Garcia and Martinez‐Arbelaiz (2002:287) describe learners frequently using “formulas” of the type “What is X?” and language ‘chunks’, our students preferred to use the incorrect phrase “What means X?” despite repeated attempts to correct the error. Regardless of which formula they use, it is noticeable that language was brief and to the point, the goal being to minimize typing time that affects synchronous online communication. In the face‐to‐face classroom on the other hand, students often resorted to more complex formulas such as “Could/Can you tell me what means X?” The grammatical mistake is once again present (its basis in a loan translation that is deeply rooted in English speakers of Romanian origin), however, there is clear evidence of an attempt at more complex functional structures. It is the face‐to‐face, the real environment that boosts the digital immigrant’s confidence in being more extrovert in communication when the educator’s presence is felt as real, as a reliable ever present helping hand.

4.2 Cultural awareness The LMS–integrated game that trained the troops who participated in the operations in Afghanistan is not yet SCORM based. However, it gives the instructors the possibility of tracking the learning progress and evaluating the level of knowledge acquisition. In the face‐to‐face English terminology module, cultural awareness was trained by role‐plays and survival scenarios, by situational dialogues where the educator turned into a back‐ stage orchestrator and left the teaching front stage empty. Again, going back to the virtual environment, students benefitted from alternatives and courses to select for their own preparation, tailored for individually envisaged deployment assignments, due to the following features: the possibility to chose from various situations; the experience of an escalated /decreased state of conflict based on personal choices; action versus instant gratification; freedom of choice and access to extra information by seeing the Reference boxes; the conflict state indicator, which provides a sense of reality, adding to an immersive attitude conducive to a better jeu‐de‐role. Moreover, linking the information in the course slides to information from the Theatre of Operations‐ the real life environment, immersion is thus augmented.Hence, the scenario presented in a cascade slide‐like presentation is not fancy anymore but a real‐life snapshot designed as a single‐player game and collaborative activities can be created by means of forums and survival cases scenarios, coached by educators, creating a bridge to the face‐to‐face interactions that all students had been exposed to, during previous training. Retention and transfer for this course are ensured by the state of flow, which in this case is relative, yet the conflict‐escalating indicator in cases when the learner makes innapropriate choices is a quite strong immersive device. The multiple‐choice situations and their consequences, with alternative routes to be taken according to the answer, is another way of ensuring transfer –a bad choice leads to life‐threatening consequences. The easy‐to‐access button for further references along with indications for what should have happened, (given the answer is wrong) brings help to internalize the information from the theoretical support. As far as students’ outcomes are concerned in the cultural awareness module, the following aspects were revealed (Ott, Popescu et al. 2012):

External expectations: to comply with someone else’s instructions; to fulfill the expectations or recommendations of someone with formal authority.

Social welfare: to improve ability to serve mankind, prepare for service to the community, and improve ability to participate in local community work (Civilian‐military cooperation‐CIMIC jobs)

Cognitive interest: to learn for the sake of learning, seek knowledge for its own sake, and to satisfy an inquiring mind, to understand and comply with a different culture.

From the educator’s perspective:

382

Maria‐Magdalena Popescu et al.

even if the game is oriented to the self improvement of knowledge and soft skills regarding cultural awareness in Afghanistan, interaction with the educators is required. The educators establish the main educational objectives in accordance with the users’ interests (mission in theaters), give them additional material to study before they start playing the game and assist them before and during the game.

the possibility to chose from various situations, having an escalated /decreased state of conflict based on personal choices ‐ action vs. instant gratification, freedom of choice and access to extra information via Reference boxes along with the conflict state indicator with alternative routes to be taken according to the answer are monitored by the educator who tailors future post‐game activities in case signals of high conflict state are shown. This means the student still has to improve his knowledge so the educator will provide more input than the game offers.

In our presently debated LMS course, the language focus was second‐stage, cultural awareness being the main goal of the course. However, the working language was English so lexical and syntactical elements could be monitored and also improved, clarified or taught. These linguistic functions were given the opportunity to be practiced / clarified via chat rooms or forums either among students or between students and the educator himself. Moreover, the linguistic approach that complemented the cultural awareness phase was “given the floor” during the assessment preparation stage, when all students had to deliver a briefing. This is the stage when indeed the focus was on lexical production rather than on linguistically correct statements. The improvement was performed asynchronously in the LMS course, while in the face‐to‐face stage the correction could be performed instantly, synchronously. Sometimes this adds value to the teaching process given that students are older, and mostly‐ digital immigrants.

5. E‐assessment in the ESP military environment The final evaluation of both the 40 non‐intensive online course students and the 35 in the intensive face‐to‐ face course consisted in delivering a 15‐minute military briefing with power point presentations used as visual aids. The briefing was based on a given scenario of an authentic/plausible situation in a theatre of operations such as Afghanistan. The subtasks consisted in selecting the information for the scenario that has the additional role of enhancing cultural awareness, conceiving the After Action Report‐power point presentation about the scenario, and delivering the briefing per se. The first two stages coincide for both face‐to‐face test‐ takers and online test‐takers, but the delivery of the briefing is performed differently by the two groups: synchronously for the face‐to‐face test‐takers, and asynchronously for the online test‐takers. The speech is evaluated in real‐time in the face‐to‐face environment while in the LMS context the speech is recorded and uploaded in an audio file. In both cases, though, we are dealing with authentic assessment. However, limitations exist in the asynchronous assessment. In the case of our two courses, students’ performance on delivering the military briefings, the rubrics of the rating sheet include the following rating criteria: task fulfillment, lexical control, structural control, delivery, output, and communication strategies. Rubrics, as Mathur & Murray state (2006:254), “provide a standardized scoring guide, which includes clear criteria and standards for assessing behaviors of performance“ as they “clearly divide a task into subtasks and assist the individual learner as well as the instructor in identifying evidence of the degree to which these tasks have been accomplished”. Both in the face‐to‐face and in the online assessment, the criteria of task fulfillment (the logical organization of a briefing, conveying the message contained in the scenario), lexical and structural control are checked similarly. However, delivery is considered successful provided that pronunciation, stress and intonation are reasonably supportive of conveying the meanings included in the message of the briefing in synchronous delivery as it benefits from the non‐verbal communication attributes. This may be to the detriment of the online asynchronous assessment of the speech uploaded in an audio file. Should things be different with this element (e.g. using videoconferencing) the time for delivery and non‐verbal communication elements may also be similar to the face‐to‐face assessment. Yet, technical and financial constraints hindered us from performing in a live‐like manner. Last but not least, the output criterion (the ability to produce extended discourse), is assessed similarly in both environments, yet hindrances appear as the audio file imposes a time frame for recording; subsequently, the online test‐takers need to have a synthetic approach to things, imposed by technical limitations. Finally, communication strategies (eliminating misunderstandings even if paraphrase and circumlocutions are used) can be assessed in both cases similarly, mentioning though that in the LMS case the sample is more relevant as it can only be recorded once, without any come‐backs, thus providing a speaking sample similar to reality, where we cannot “edit” our statements in a real‐time conversation.

383

Maria‐Magdalena Popescu et al.

6. Conclusions When referring to online /vs./ face‐to‐face learning for adults, teachers should be aware of the fact that adults are responsive to external motivators (grades, verbal praise etc.), but still internally motivated. Thus, online transformation teaches adults how to maximize their learning without the benefit of having a face‐to‐face discussion with their teacher. Besides, it has been proven that more mature learners require less instructor control and course structure as they can be more self‐directed in their learning, while less mature learners require more instructor guidance and course scaffolding (Canning & Callan, 2010; Kenyon & Hase, 2007). Therefore, while in the face‐to‐face intensive English module the accent was placed more on linguistic aspects to allow more for the immediate correction phases, in the online non‐intensive English‐based cultural awarenss pre‐deployment training, the focus was more on soft skills than on declarative knowledge, more on affective skills, social and inter‐personal, with an indirect meta‐cognitive shift provided in the self‐evaluation session followed by the immediate rehearshal and appeal to learning resources. The conclusion can be that LMS ‐ and TEL implicitly ‐ is best at attaining goals on procedural knowledge and affective skills, while declarative knowledge is better fostered by face‐to‐face learning environments. Since the military personnel needs to be deployable at all times, TEL instruction is of preference for attaining or reinforcing skills other than declarative (theoretical input) in a short time, a cost effective manner and on a „whenever‐wherever” availability base (Popescu, Romero & Usart, 2012). As far as e‐assessment is concerned, despite its limitations and in situations that are familiar in the military, when students are away from the exam site, on missions in theatres of operations, online testing may be the only working evaluation method for all basic skills.

References Blake, R. (2006) “Two Heads Better Than One: C[omputer] M[ediated] C[ommunication] for the L2 Curriculum”. In Donaldson, R., Haggstrom, M.A. (Eds.), Changing Language Education through CALL, Routledge, New York, pp 229‐ 248. Boekaerts, M. & Corno, L. (2005) “Self‐regulation in the classroom: A perspective on assessment and intervention”, Applied Psychology: An International Review, 54(2), pp 199‐231. Brown, H. Douglas (2004) Language Assessment. Principles and Classroom Practices, Pearson Longman. Canning, N. & Callan, S. (2010) ″Heutagogy: Spirals of reflection to empower learners in higher education”, Reflective Practice, 11(1), pp 71‐82. Carlson, A. (2001) Authentic learning: What does it mean? Retrieved July 6, 2004 from http://pandora.cii.wwu.edu/showcase2001/authentic_learning.htm Cranton, P. (2000) Planning Instruction For Adult Learners, Toronto: Wall & Emerson, Inc. Department of Defence ( 2009) Army Culture and Foreign Language Strategy, USA. Dillenbourg, P. (1999) “What do you mean by ‘collaborative learning’?”. In P. Dillenbourg (Ed.), Collaborative‐learning: Cognitive and computational approaches, Amsterdam: Pergamon, ElsevierScience, pp 1‐16. Dunn, L., Morgan, C., O’Reilly, M., Parry, S. (2004) The Student Assessment Handbook. New Directions in Traditional & Online Assessment, Routledge Falmer. Dweck, C.S. (2002) Beliefs that make smart people dumb, in R.J. Sternberg (Ed.), Why smart poeple do stupid things, New Haven: Yale University Press. Fernandez‐Garcia, M. & Martinez‐Arbelaiz, A. (2002) “Negotiation of meaning in nonnative speaker‐nonnative speaker synchronuous discussions”, CALICO Journal, 19(2), pp 279‐294. Hase, S. (2009) “Heutatogy and e‐learning in the workplace: Some challenges and opportunities”, Impact: Journal of Applied Research in Workplace E‐learning, 1(1), pp 43‐52. Hase, S. & Kenyon, C. (2007) From andragogy to heutatogy, in Ultibase Articles, retrieved from http://ultibase.rmit.edu.au/Articles/decoo/hase2.htm Kamenetz, A. (2010). DIY U: Edupunks, Edupreneurs, and the Coming Transformation of Higher Education. Whiter River Junction: Chelsea Green Publishing Company Kirschner, P.A., Martens, R.L., & Strijbos, J.W. (2004) “CSCL in Higher Education? A Framework for Designing Multiple Collaborative Environments”. In Strijbos, J.W., Kirschner, P.A., Martens, R.L. (Eds.), What We Know about CSCL and Implementing It in Higher Education, Kluwer Academic Publishers. Knowles, M. S. & Assoc. (1984) Andragogy in action: Applying modern principles of adult learning, San Francisco: Jossey‐ Bass Publishers. Kuit, J.A. & Fell, A. (2010) Web 2.0 to pedagogy 2.0: A social‐constructivist approach to learning enhanced by technology, in Critical design and effective tools for e‐learning in higher education: Theory into practice, United States: IGI Global, pp 310‐325.

384

Maria‐Magdalena Popescu et al. Levy, M. (2006) “Effective Use of CALL Technologies: Finding the Right Balance”. In Donaldson, R., Haggstrom, M.A. (Eds.) Changing Language Education through CALL, Routledge, New York, pp 1‐18. Mathur, S. & Murray, T. (2006) “Authentic Assessment Online: A Practical and Theoretical Challenge in Higher Education”. In William, D., Howell, S.L., Hricko, M. (Eds.), Online Assessment, Measurement and Evaluation. Emerging Practices, Information Science Publishing, pp 238‐258. Mulkey, J. (2001) “The anatomy of a performance test”, Certification Magazine, (May), pp 58‐62.Muller,J.(2003) “Authentic assessment toolbox”, [online], http://jonathan,muller.faculty/nocrtl.edu/toolbox/whatisi.htm Olsen, J.B. (2006) “Performance Testing: Validity Issues and Design Considerations for Online testing”. In William, D., Howell, S.L., Hricko, M. (Eds.), Online Assessment, Measurement and Evaluation. Emerging Practices. Information Science Publishing, pp 259‐274. Ott M., Popescu, M.M., Romero M., Usart M., & Earp J. (2013) "Supporting Human Capital development with Serious Games: an analysis of three experiences”, Elsevier Editorial System(tm) for Computers in Human Behavior, Computers and Human Behaviour Popescu M., Romero M., Usart M. (2012) "Using serious games in education‐ a serious business for serious people”, ICVL c3.icvl.eu/2012/proceedingsShareProceedings. Rakova, N.A. (2010) Andragogicheskaya y pedagogicheskaya modeli obucheniya – sopostoviteljnyi analiz, Udk 37.013.83 Raybourn EM, Deagle E, Mendini K, Heneghan J – 2005)‐ Adaptive thinking and leadership Simulation game training for special forces officers, I/ITSEC Proceedings, Orlando, Florida, USA Von der Emde, S., Schneider, J. & and Kötter, M. (2001) “Technically Speaking: Transforming language learning through virtual learning environments (MOOs)”, The Modern Language Journal, 85(2), pp 210‐225. Weegar, M.A. (2012) A Comparison of Two Theories of Learning ‐ Behaviorism and Constructivism as Applied to Face‐to‐ Face and Online Learning, E‐Leader, Manila, 2012 Wheeler, S. (2011) Learning with e’s: Digital age learning, [Blog post], retrieved from http://steve‐ wheeler.blogspot.com/2011/07/digital‐age‐learning.html. Winne, P.H. & Perry, N.E. (2000) Measuring slef‐regulated learning, in P.Pintrich, M. Booekaerts, & M. Seider (Eds.), Handbook of self‐regulation, Orlando, FL: Academic Press, pp 531‐566. Wiggins, G. (1990) The Case for Authentic Assessment, Washington, DC: ERIC Clearing house on Tests Measurement and Evaluation, American Institute for Research.

385

The Disruptive Potential of e‐Learning in Academe and Beyond: A Futuristic Perspective Ali Raddaoui Program of Global & Area Studies / Department of Religious Studies, University of Wyoming, Laramie, Wyoming, USA araddaou@uwyo.edu Abstract: E‐learning theory and practice have been heavily focused on e‐learning as a tool, medium and space of formal instruction. This paper is an invitation to think of e‐learning beyond the domain of education, and to consider its implications for social stability and the standing of knowledge in the emerging e‐knowledge society. This paper adopts a futuristic stance, and sets itself a dual objective. The first is to distinguish two types of e‐learning: academic e‐learning, and the extra‐academic, serendipitous, unbounded, social e‐learning. The second is to characterize and imagine the corresponding levels of change these two types of e‐learning are and will be introducing into our future learning practices, concepts of knowledge, and social realities. This paper rests on a review of the literature on formal, academic e‐learning with focus on the transformations this type of e‐learning generates at the level of pedagogy, the sense of community among students and the standing of knowledge. Subsequently, the transformations registered at the level of academic e‐ learning are transposed and extrapolated into the type of informal, didactically‐unmonitored, social e‐learning, to gauge their anticipated nature and degree. This paper recognizes the important repercussions of e‐learning on traditional learning and the shift in the source of knowledge from traditional cartels to more distributed, more egalitarian, social media‐driven networks. Exploration of the emerging, unbounded, social e‐learning trends seem to lead to a destabilised social situation where formerly‐acknowledged and socially‐sanctioned reference points are weakened to give way to a plethora of truths, realities and representations. Keywords: e‐learning, unbounded e‐learning, social media, web 2.0, subversion, futures studies

1. Introduction An enduring criticism of e‐learning research for the past several years has been that e‐learning is examined primarily from the front‐end of information and communication technologies. As a result, e‐learning theory and pedagogy are relegated to backstage (Haythornthwaite and Andrews 2011; Remtulla (2008, 2010; Enonbun 2010; Halse and Mallinson 2009; Beard et al. 2007; Omar et al. 2011). The flow of technological affordances onto the learning, teaching and knowledge‐making arenas has meant that the theoretical enterprise has yet to catch up with the long‐term impacts of this metaphorical elephant in the room both at the level of the formal business of education and more importantly out into the wider social e‐learning environment (Wood 1995; Remtulla 2010). This paper adopts a futuristic perspective and sets itself a dual goal. The first is to distinguish two kinds of e‐ learning, one that takes place primarily in formal education while the second is unfolding outside formal education, into the world of internet users who encounter or build their own informal or chance learning agendas. This distinction is not new in itself (Naidu, 2003; Goyal 2012, Halse and Mallinson 2009; Haythornthwaite and Andrews 2011), but it represents an invitation for us to map out the transformations e‐ learning is and will be generating at the level of formal schooling but crucially at the level of the wider, unbounded, out‐of‐school, e‐learning. For each class of e‐learning, but evidently more in free, social, unbounded e‐learning than in school‐sanctioned e‐learning, significant transformations are taking place which are projected to impact the pedagogic process, the relationships between learners and knowers, the knowledge‐construction process, and the very status of knowledge. In a certain sense, this paper seeks to outline an e‐learning‐grounded pedagogical‐cum‐ sociological order where traditional players (the curriculum, teachers and authors, the educational establishment and their governing structures) will be challenged as a result of a perceptible shift in the locus of power from these organized and centralized structures to networked individuals and communities who conduct their e‐learning outside the norms and knowledge systems maintained by the educational institution. If, as Enonbun (2010) asserts, technology is visibly leading the way in learning and education as well as in all other fields of endeavor “what remains vague […] is the direction and pace of such changes…” (p. 18). For this paper, two questions need addressing: (i) what is the nature and extent of the transformative, destabilising

386

Ali Raddaoui potential that the “e” in e‐learning is injecting into academic learning? And more importantly, (ii) what forms will this destabilisation take outside academic settings and into the uncontrolled, unbounded, cyber e‐learning world? Before attempting an answer, two definitions for academic e‐learning and unbounded e‐learning are in order.

1.1 Definitions By academic e‐learning is meant formal, mainstream e‐learning educational institutions offer for students by teachers, through the internet, using information and communication technologies, in order to achieve clearly set learning goals circumscribed by official curricula for certification purposes. This kind of e‐learning can be characterized as a closed‐circuit platform since only students formally enrolled have access to its syllabi, learning objects, as well as instructional and assessment procedures. These are often hosted in one electronic learning platform or another, such as Moodle, Sakai, E‐Companion, or Blackboard Vista. In contrast, unbounded e‐learning includes all electronically‐mediated and shaped learning conducted by individuals and groups for purposes other than formal certification and workplace training. This type of e‐ learning is said to be ‘unbounded’ primarily because it is not closed to a group of students formally enrolled in a learning program, but represents materials, media and processes internet users have easy access to. Nichani (2001) speaks of chance, serendipitous [e‐]learning experiences as being what the Web is all about. A different angle from which unbounded e‐learning can be viewed would be from the perspective of learners. Haythornthwaite and Andrews (2011 p. 19) define non‐formal/informal e‐learners as “those searching the web for entertainment, facts, news, or opinion, or for a dialogue and engagement in knowledge construction” and those “browsing the net in the way many browse book shelves in the traditional library, not sure what they want to find, but ready to follow an interesting thread.” In this wide acceptation, unbounded e‐learning is defined as “perpetual, sustained over a lifetime and enacted in multiple, daily occurrences” (Haythornthwaite and Andrews 2011, p. 2).

1.2 Approach With these working definitions in place, what we propose to do is to project the properties of academic e‐ learning unto unbounded e‐learning spaces and imagine how they will unfold and evolve there. The goal is to extrapolate the future, formulate “prospective descriptions” and envision possible scenarios (Kinsley 2011, p. 232), without claiming that these constitute foregone conclusions. By way of anticipating these scenarios, we will say that e‐learning activity, conducted in socially‐unbounded spaces, is likely to generate significant transformations described in these terms: “less than orderly change processes”, “risky or ethically questionable” spilling into “chaotic behavior”, and potentially “unplanned, unsought and unanticipated consequences” (Coates et al. 2001, pp. 4‐5). Of course, extrapolation from pedagogical, dialogical and theoretical observations on formal e‐learning contexts, into the wider, unbounded e‐learning platforms is fraught with a degree of uncertainty associated with treating psychological, social, and technological phenomena whose trends have yet to be systematically described and theorised. To minimise this uncertainty, it will be useful to start with a discussion of formal e‐learning actors and their relationships, the curriculum, the learning and teaching processes, and the body of knowledge constituting an agreed reference point. Then, in recognition of the fact that academic e‐learning is, overall, a largely controlled environment, the next step would be to exaggerate its properties in the extra‐academic, uncontrolled, unbounded, social e‐learning domain and project them into the future so as to visualize the extent of transformation. This is what the remainder of this paper is dedicated to.

2. The transformative potential of academic e‐learning There is wide agreement that the impact of electronically‐mediated learning goes far beyond the medium. Beard et al. (2007), Palfrey and Gasse (2012), Remtulla (2008, 2010), Enonbun (2010), Halse and Mallinson (2009), and others are forthright in their critique of technical, artefactual, software and hardware, instruction design and generally technology‐led approaches to e‐learning. Haythornthwaite and Andrews (2011) stress that “e‐learning is not a diminished version of learning” (p. 2) and suggest that migration from linear text to hypertext signals change in the very nature of learning. Beard et al. (2007) and Halse and Mallinson (2009) underscore the need for an e‐learning‐specific theoretical perspective. Remtulla (2010) calls for a new

387

Ali Raddaoui pedagogical theory which goes so far as to affect the very notions of ontology, epistemology and pedagogy. In what follows, we propose to zero in on the transformative power of formal e‐learning in comparison with traditional, instructor‐led teaching. Transformations appear at the level of the individual e‐learner, the level of e‐learners as a group, and finally the level of knowledge construction. This section will serve as a stepping stone to map out the future of e‐learning out into the social, informal, unbounded context.

2.1 The e‐learner’s voice gets a boost Haythornthwaite and Andrews (2011) characterise conventional learning as “a hierarchical power system” (p. 57), with the teacher acting as mediator between a body of knowledge and the learners. This knowledge is created by authors and disseminated by publishers, both of whom constitute a relatively closed and closely guarded system. Grosso modo, this system looks like a pyramid, with publishers, editors, researchers and authors seated at the top. The State apparatus, through the Education Department and its various agencies, maps out curricula for schools, which are then packaged into course syllabi often with their corresponding textbooks. The actual, physical, classroom teacher intervenes at this point and mediates between the syllabus and the students. Students remain at the receiving end, charged with absorbing, digesting, acquiring and regurgitating materials. One question we need to pose now is how far e‐learning destabilises this configuration, where the teacher, occupying center stage, incarnates knowledge on the “classroom earth”. What differentiates e‐learning contexts from face‐to‐face contexts is that neither the teacher nor the students are physically seen. Haythornthwaite and Andrews (2011) point out that loss of such visible signs as gender, race, dress, status, provenance is compensated for by a positive gain they term as “status flattening” (p. 13). By this is meant that the respective statuses of the e‐learner, the group as a whole, and the teacher are redefined in the direction of leveling. As an illustration, contributing to a threaded discussion in a forum creates an even‐playing field, which gives those otherwise associated with low status a voice as powerful as those who would have been accorded high status in face‐to‐face environments. Thus, in lieu of a hierarchical system reflecting the out‐of‐ school power structure, we land in an environment where the learner can freely speak their mind online without being boxed into a subordinate position. Formal e‐learning offers a host of additional advantages. With course materials now presented on the screen, it is up to the learner to decide from a plethora of materials which to consult, which to skip, and at what point in time (Haythornthwaite and Andrews 2011). Whereas in regular instructor‐led training, “students are pushed through a course in a specific time frame” (Goyal 2012, p. 240), e‐learning moves at the learner’s pace. Beyond what we now take for granted, these features carry great significance: in traditional teaching, the teacher is largely in charge of designing and implementing the students’ learning experience (even when inspired by such progressive learning theory as constructivism); formal e‐learning gives rise to ecology where the e‐learner exerts firmer control on their learning dashboard. Haythornthwaite and Andrews (2011) add that the learner’s ability to consult outside sources which may be at odds with their teacher’s opinion, and to exert a larger measure of autonomy, expands the learner’s intellectual legroom to question the teacher’s authority. This questioning does not just happen at the level of the individual e‐learner, but the group as a whole also garners newfound‐powers.

2.2 The newfound power of e‐learners as a group In face‐to‐face instruction, the teacher serves as main driver, initiates and answers questions, designs and implements teaching moves, and operates as anchor point. In contrast, e‐learning, properly conducted, has the merit of turning learners into a community or network whose members tap into and recognize each other as resources. The sense of network among them means that because of their status as learners with a common purpose, no one can claim a supervisory capacity over the others; the status of each is more or less comparable to the status of the others. This has the effect of rendering transactions more or less equal, and not power‐laden. By the same token, the teacher is no longer the only person scaffolding the learning situation; scaffolding is now partly shared by the group. This feeling that learning is achieved by the e‐network, with a degree of teacher involvement, is paramount. Gradually, an understanding develops that the presence of the teacher as mediator, guardian, and dispenser of knowledge is not a precondition for learning, and that the network is more in charge of its own learning. According to Haythornthwaite and Andrews (2011), participatory online

388

Ali Raddaoui learning “entails instructors ceding leadership and control of learning, giving it over to participants, and encouraging new forms of co‐learning pedagogy” (p. 49).

2.3 Democratization of knowledge construction in e‐learning contexts Another area with a significant transformation coefficient is the sphere of knowledge apprehension and creation. We will revisit this area, but for now, we content ourselves with a brief juxtaposition of the status of knowledge before and after the emergence of e‐learning. As Haythornthwaite and Andrews (2011) point out, this knowledge exists, is closely guarded, often comes in print form, and is taken on its own terms. Its static, print form indexes stability, validity, and durability. In view of the multi‐layer editing and filtering process, a fitting description of it would be to say that it is hegemonic; commenting upon it, editing it, altering it, and otherwise questioning it is out of the reach of the average person. As knowledge migrates from print to the digital world, the e‐learner’s encounter with it is no longer one of veneration and stupefaction; e‐learners can readjust its size, break it into digital pieces, and annotate it (Haythornthwaite and Andrews 2011). Knowledge can be remixed, recast, translated, rephrased, and even quoted without a bibliographic reference. From being an iconic symbol of status and power, knowledge, now digitized, gets reified, handled, dissected, critiqued, “incorporated as part of a dialectic or at least dialogical exchange” (Haythornthwaite and Andrews 2011, p. 58), and somehow debunked. The e‐learner’s voice becomes in some ways equal to the author’s voice, and the once uncontested promontory of the author is put into question by the new e‐learning configuration. All of a ‘historical’ sudden, e‐learners, still at formative stages, find they too can rate posts, critique positions, contribute content, and weaken the grip previously held by the knowledge and publication cartel. In so doing, they approach knowledge and authority increasingly like “experts” and “entrepreneurs” (Haythornthwaite and Andrews 2011) and bask in the advantage of being at once “a receiver and sender of “broadcast”” (Brown, 2000, p. 12) What transpires from this section is that academic e‐learning has the effect of disrupting deep‐seated pedagogic and knowledge structures, paves the ground for a shift in the balance of power and evens the playing field between the teacher, the learner, the community of learners, and the authors of knowledge. The object of the second section of this paper is to superpose this formal e‐learning map on unbounded e‐learning as experienced in the socio‐cyber theater.

3. Unbounded e‐learning: How far does disruption go? In order to extrapolate from the current state of formal e‐learning into unbounded e‐learning, we raise the following questions: (i) how prevalent is unbounded learning?, (ii) What are the anticipated future consequences of e‐learning now practiced away from controlled academic spaces out into cyberspace?

3.1 The volume of unbounded e‐learning According to ITU, in 2013, 39% of the world population has access to the internet, while 41% of world’s households have that same access. Mobile cellular penetration stands at 96% (ITU, 2013), with over a billion people using mobile devices as their primary internet access point (Google 2012). Social networking sites too show revealing penetration figures: Facebook: 12.1% (Internet World Stats 2012); YouTube: over 1 billion (Reuters 2013); Twitter: over half a billion, (Statistic Brain 2013), to name just the most prominent social media through which informal e‐learning is conducted. It will be crucial to chart how these figures have progressed from the early days of the Web 2.0 era, and then to model their growth in the years to come, as the internet becomes as imperceptible as televisions sets are in most households.

3.2 The extrapolation At the core of the question on the disruptive power of unbounded e‐learning is an understanding that received knowledge as we think we know it serves as an anchor of and foundation for social stability. The emerging unbounded e‐learning context can be characterized as one in which there would be no single, more or less unified, and canonical body of knowledge that unbounded e‐learners can draw into as an agreed reference point. In some ways, knowledge, though abundant, mostly free, and available at a keystroke, is fragmented, diversified, contradictory, and altogether not so coherent. Secondly, because of the sheer mass of people handling knowledge, information and data, a novel reality is hanging upon the handlers, consumers, producers, and players of and with knowledge, which is that there is not a stable and time‐tested body of

389

Ali Raddaoui knowledge that represents a more or less universally acknowledged source. Thirdly, because learning from free, open, unbounded, and uncontrolled online environments is not firmly grounded into an agreed ontology and informed by an established epistemology, a sense of disorientation emerges, spilling into something of a definitional crisis. The new dynamics of knowledge creation appear to be unseating the traditional powers that served as knowledge vetters, regulators, creators, and disseminators. We are now in a new knowledge construction stratosphere where constellations of knowledge can form away from the oversight of traditionally‐trained, certified, and socially recognized knowledge makers. In a sense, the traditional knowledge‐construction paradigm is undergoing a major reshuffle described in terms of destabilisation.

3.3 The process of destabilisation: the broad picture One way to broach destabilisation is through exaggerating the contrasts between three learning modes, instructor‐led learning, academic e‐learning and unbounded e‐learning. Let us state initially that face‐to‐face instruction is a closed system where students sit in a walled classroom. Between students and the curriculum stands a teacher with credentials granted by the education system. The curriculum is co‐designed by the teacher, education authorities, and the authors of knowledge; knowledge is housed within the learning institution in the form of a physical library. The knowledge inside the library is considered a commodity available to consumers against an agreed cost. Being mostly in the form of books and journals, knowledge can be considered with a stretch of imagination something of a fixed entity to be relayed to learners. It is controlled by a cartel of publishers and knowledge makers. Formal learning takes place essentially within this closed system, and the whole process rests on a binding, un‐resisted contract between learners, teachers, parents and the education system. By contrast, academic e‐learning is a porous and less closed system. Though the teacher and students are distributed in space and may operate in different time zones, the teacher and the curriculum are still core components. The curriculum constitutes a strong force of pull; students achieve the outcomes set by the educational authorities based on knowledge hosted in the learning management system or virtual learning environment. However, due to the porous nature of the system, learners have access to multiple resources, human and digital, that are not necessarily part of the syllabus. The teacher’s grip is still there, since it is they who determine passing and failure, but it is weaker as it is bypassed by a plethora of e‐knowledge, materials and networks that learners have access to, and which are under nobody’s control. Let us now superpose this map onto unbounded e‐learning. Chief among its features is that it carries no enforced curriculum; individual members and the networks they belong to are largely free to choose their learning goals, to build their networks, and to determine the processes of their learning. Nor is there a formally‐certified teacher or knower per se to frame, plan and assess learning since formal, academic qualifications are not the end result. Thus, these learning netizens float in an online world, under no one’s particular oversight, and are not subject to any binding contract, except what they determine for themselves, or what they chance to learn as a result of random, unplanned, and serendipitous encounters.

3.4 The status of knowledge in unbounded e‐learning What about knowledge and its sources? Firstly, the meaning of knowledge as a monopoly of “anointed” writers, authors and publishers is weaker in this unbounded e‐learning space. Whereas knowledge was closely guarded in earlier systems, being a commodity offered against a price, now, the emergence and power of open‐access journals, massive open online courses, free online libraries, Google books, the blogosphere, and the .org/net/info domains has weakened the grip of the traditional knowledge cartel. Knowledge is going through a process of rapid de‐commodification as it is mostly free. Secondly, abundance of web resources, the ease and speed with which these are created and circulated, and the relative irrelevance of gatekeepers, will make it extremely difficult for learners to evaluate the “knowledge value” of these resources: standing of the author, recency of content, soundness of methodology, degree of bias, etc. For example, a simple Google search term such as “e‐learn*” yields as many as 2,330,000,000 hits. Which of these will the average e‐learner end up visiting from this unregulated, free, e‐content market is anyone’s guess. Superabundance of freely available data often not produced by experts puts the learners in a situation where the many ‘knowledge bases’ from which they draw may be conflicting, inconsistent, and not scientifically rigorous.

390

Ali Raddaoui In addition, because of the complex of variables characteristic of the networks e‐learners subscribe to, different types of knowledge are produced that reflect the understanding of each network. Knowledge, which used to be Received Knowledge, with capital K, created by powerful research institutions operating within known and agreed paradigms, is now generated by disparate e‐networks. In lieu of a body knowledge heretofore considered more or less homogeneous, cohesive, and generally supportive of a widely‐accepted set of social values, the knowledge internet surfers encounter, author, and comment upon, is fragmented, dissonant, and produced in parallel to and despite the traditional and conservative knowledge actors. This is a tentative account of how fragmentation occurs: learners in unbounded e‐learning spaces come across diverse and conflicting bodies of knowledge undergoing transformation by the minute. These are the sorts of environment where it is “extremely difficult to exercise direct power over another individual” (Torok, 2013), and to mandate that they study a particular author or group of authors, rather than others. As a result of this random exposure, e‐learners lose a unique and unifying reference point that has traditionally made for stability and continuity. It is the claim of this paper that this loss of largely‐adhered to reference points, consolidated by the unmonitored nature of unbounded e‐learning, has the potential to generate contradiction, division, disharmony, unrest, and probably chaos. Finally, thanks to these unbounded e‐learning spaces, what obtains is a situation where actors eventually move from the position of learning from the e‐network, to action, with the new space serving as something of a simulator. Perceptual changes, a condition for learning, are seeded, and the next logical step for e‐learners in this unbounded space is action. Action gains momentum in the electronic space and then migrates out into real life. This is deregulated, not fully monitored, and largely uncontrollable action undertaken by people outside the purview of the generally conservative watchdogs of school and the “established” order. The action taken signifies that the establishment is weak or is at least becoming somewhat irrelevant. The by‐word is: learn anything; believe what you will, say what you like, publish everywhere, then, act on what you know in cyberspace and out into society. As a foretaste of this unfolding scenario, Julian Assange releases his earth‐shaking WikiLeaks; the youth of Tahrir Square rock the Kasbah Palace in Egypt (Agathangelou & Soguk 2011), overthrow President Hosni Mubarak’s authoritarian regime, then turn against the first freely‐elected, civilian president in Egypt’s history; minorities speak louder than ever; online radicalisation is rampant and the internet becomes “an online institution for gathering and coordinating (these) marginalized individuals (Torok 2013, p. 2); disaffected Brazilian youth rise against the State and declare ““We’ve come from Facebook”, “We are the social network”, and in English: “Sorry for the inconvenience, we are changing Brazil”” (Mason 2013). ‘Anonymous’ hackers from all four corners of the earth wreak havoc on establishment websites; consultant Edward Snowden’s disclosure of US National Security Agency secrets is called “A psychologically compelling mess” and it becomes difficult to assign the ‘whistle blower’ or ‘traitor’ label to him (Essig 2013). Incoherence reigns and spawns such Facebook comments as: “Beware of smoking Marlboro so you don’t help America triumph. Instead smoke hash and stand by your brethren in Afghanistan”. This definitional, social and linguistic chaos may be a prelude for what Mason (2013) terms “a permanent revolution”.

4. Conclusion 4.1 Summary and conclusions This paper sought to demonstrate that academic e‐learning brings about transformations leading to weakening the teacher’s traditional authority, and consolidating the e‐learners’ voices. As knowledge becomes more widely available and more easily authored, shared, and critiqued, it loses the hegemony it once had and its erstwhile makers can no longer claim it as monopoly. Once carried from academic e‐learning into unbounded e‐learning, these transformations appear to evolve into less mitigated and more extreme forms of destabilisation. Formerly‐acknowledged and socially‐sanctioned reference points are weakened to give way to a plethora of truths, realities and representations. Unbounded e‐learning appears to have the potential of unseating long‐established cannons of what knowledge is, where it is located, how it is authored, disseminated, and consumed. Demotion of a more or less unified ontology that once generated a cohesive, unconditionally‐adhered to body of knowledge, and the emergence of alternative knowledges constructed by differential epistemologies and

391

Ali Raddaoui variously defined communities indexes a significant paradigm shift. According to the scenario built in this paper, unbounded e‐learning and social media are expected to help shape an unstable, chaotic and fragmented social setting, illustrations of which we are currently witnessing in major, online and offline, global, sociopolitical uprisings.

4.2 Limitations and avenues for further research This paper signals the need for e‐learning researchers and practitioners to bridge the gap between academic e‐ learning applications and theorizations on the one hand, and the social implications of out‐of‐school, unbounded e‐learning on the other. It is suggested that unbounded e‐learning has a significant transformation and destabilisation potential. Further theory‐informed research on this initial onslaught is needed in order to more accurately capture, gauge, imagine, and manage the transformations unbounded e‐learning is expected to generate on the epistemological and social planes.

References Agathangelou, A.M. & Soguk, N. (2011, October) Rocking the Kasbah: Insurrectional Politics, the “Arab Streets”, and Global Revolution in the 21st Century, Globalizations. Vol 8, No. 5, pp 551–558. Beard, B., Wilson, J.P., and McCarter R. (2007) Towards a Theory of e‐Learning: Experiential e‐Learning. Journal of Hospitality, Leisure, Sport and Tourism Education. Vol 6, No. 2, pp 3 – 15. Brown, J.S. (2000) Growing up Digital: How the Web Changes Work, Education and the Ways People Learn. Change. (March/April Issue). pp 11‐20. Coates, V., Farooque, M., Klavans, R., Lapid, K., Linstone, H. A., Pistorius, C. and Porter, A. L. (2001) On the Future of Technological Forecasting. Technological Forecasting and Social Change, No. 67, pp 1–17. Enonbun, O. (2010) Constructivism and Web 2.0 in the Emerging Learning Era: A Global Perspective. Journal of Strategic Innovation and Sustainability, Vol 6, No. 4, pp 17‐27. Essig, T. (July 13, 2013) Why The Edward Snowden Story Is Such A Psychologically Compelling Mess. Forbes. Retrieved from: http://www.forbes.com/sites/toddessig/2013/07/13/why‐the‐ edward‐snowden‐story‐is‐such‐a‐ psychologically‐compelling‐mess/ Google. (February 27, 2012). Consumers love their smartphones. Now businesses must fall in love with mobile. Retrieved from http://googlemobileads.blogspot.com/2012/02/consumers‐love‐their‐smartphones‐now.html Goyal S. (2012) E‐Learning: Future of Education, Journal of Education and Learning. Vol 6, No. 2, pp239‐242. Halse, M.L. and Mallinson, B.J. (2009) Investigating Popular Internet Applications as Supporting E‐ learning technologies for Teaching and Learning with Generation Y. International Journal of Education and Development using Information and Communication Technology (IJEDICT), Vol 5, No. 5, pp 58‐71. Haythornthwaite, C. and Andrews, R. (2011) E‐learning theory and Practice. London: Sage. Internet World Stats (2012) Facebook Users in the World. Retrieved from: http://www.internetworldstats.com/facebook.htm ITU (International Telecommunications Union) (2013) The World in 2013: ICT Facts and Figures. Retrieved from http://www.itu.int/en/ITU‐D/Statistics/Pages/facts/default.aspx Kinsley, S. (2011) Anticipating Ubiquitous Computing: Logics to Forecast Technological Futures. Geoforum, No. 42, pp 231– 240. Mason, P. (June 21, 2013) Why are the BRICs are crumbling? Welcome to the permanent revolution Naidu, S. (2003, June) E‐Learning: A Guidebook of Principles, Procedures and Practices. Commonwealth Educational Media Centre for Asia (CEMCA). Nichani, M. (June 08, 2001) Serendipitous learning. Retrieved from elearning posts: http://www.elearningpost.com/articles/archives/serendipitous_learning Omar, A., Kalulu, D. and Belmasrour, R. (2011, Winter) Enhanced Instruction: The Future of E‐learning. International Journal of Education Research, Vol 6, No. 1, pp 21‐37. Palfrey, J. and Gasse, U. (2012) Born Digital: Understanding the First Generation of Digital Natives. Basic Books. Remtulla, K.A. (2010) Socio–Cultural Impacts of Workplace E–Learning: Epistemology, Ontology and Pedagogy. IGI Global. ‐‐‐‐‐‐‐‐‐‐‐‐‐‐ (2008) A Social Theory Perspective on E‐learning. Learn Inq, No. 2, pp 139–149. Reuters. (March 20, 2013) YouTube Stats: Site Has 1 Billion Active Users Each Month. Huffington Post. Retrieved from: http://www.huffingtonpost.com/2013/03/21/youtube‐stats_n_2922543.html Statistic Brain (May 7, 2013) Twitter Statistics. Retrieved from: http://www.statisticbrain.com/twitter‐statistics/ Torok, R. (May 20, 2013) Explaining online radicalization. Extremis Project. Retrieved from:http://extremisproject.org/2013/05/explaining‐online‐radicalisation/ Torok, R. (2013) Developing an explanatory model for the process of online radicalisation and terrorism. Security Informatics, Vol 2, No, 6, pp. 2‐6. The Independent, Retrieved from: http://www.independent.co.uk/voices/comment/why‐are‐the‐brics‐are‐ crumbling‐ welcome‐to‐the‐permanent‐revolution‐8668803.html Wood, D. (1995) Theory, Training and Technology: Part I. Education & Training, Vol 37, No. 1, pp 12‐16. Electronic resources

392

What Really Happens When Educators Make and Evaluate TEL Innovations? Claire Raistrick University of Warwick, Coventry, UK c.g.raistrick@warwick.ac.uk Abstract: This social practice research focuses on what educators in higher education (HE) actually do when they make technology enhanced learning (TEL) innovations. Using the concept of self‐evaluation presented by Saunders et al. (2011) I focus on the self‐evaluative practices educators use to make judgements about the worth or value of their TEL innovations, rather than the technology itself. One prominent view, regarding TEL in HE is “that effective change will emerge by equipping the main participants with a proper understanding of their needs, and then with the ability to use technology effectively to meet them” (Mayes, 2009:46‐47). However, how is this “effective change” to be achieved? Perhaps self‐ evaluation might contribute? The methodological approach in this qualitative study is co‐construction – the researcher and participants working together to generate data through dialogical conversation before also using RUFDATA (Saunders, 2000) (an established evaluation tool). The rich data we generate provides evidence from which I typologise features of self‐evaluative practices. Participants display recurrent behaviours as they engage iteratively with the processes of self‐ evaluation; their questioning, responsive acts involving both stakeholders and reflexivity. These efforts cohere to move their TEL projects forward, from one provisional state to the next, via a process of evaluative creep which achieves pedagogical change. The significant output of this research is the SEPT4TEL (self‐evaluative practices typologies for TEL) framework which offers a robust understanding of what is meant by self‐evaluative practices in this study’s context. This new knowledge exemplifies Reckwitz's (2002) description of people as carriers of practice within their social mêlée. The SEPT4TEL framework is a means to guide and inform educators’ judgements about their TEL innovations; thereby supporting the transformative potential of TEL. It promotes authentic, systemised use of self‐evaluation within evaluative cultures where educators’ learning about their professional practice is fundamental. Keywords: self‐evaluative practices, technology enhanced learning, higher education, innovation

1. Introduction A landmark report regarding Technology Enhanced Learning (TEL) in higher education (HE) suggests “that effective change will emerge by equipping the main participants with a proper understanding of their needs, and then with the ability to use technology effectively to meet them” (Mayes, 2009:46‐47). But, how is this “effective change” to be achieved? Perhaps self‐evaluation might contribute? Overall, four domains of evaluative practice are recognised in HE: national/systemic, programmatic, institutional and self‐evaluation (Saunders et al., 2011). It is, however, “unusual to focus on evaluation practice as an object of study” (Saunders, 2011:1). Additionally, though examples of educators’ TEL innovations are widespread, educators’ self‐evaluative practices are not the focal point. Therefore this study’s focus on self‐evaluative practices when making TEL innovations is unusual. This paper concentrates on one aspect of a social practice research study which asks: ‘How do educators in HE explain what they do, and why, when evaluating TEL innovations?’ In short the answer to this question is encapsulated in the SEPT4TEL (self‐evaluative practices typologies for TEL) framework which is the significant output of this research and is detailed presently. Accordingly, the object of study is the self‐evaluative practices used by educators (teachers, researchers who teach and technologists) when they introduce a TEL innovation to enhance the learning environments in which they work and how they are convinced that their TEL innovations are worthwhile. I use the concept of self‐evaluation presented in Saunders et al.’s (2011) fourth domain of evaluative practice: self‐evaluative practice – evaluations which take place to inform the professional practice of either solitary practitioners or groups of practitioners. The nub of self‐evaluation being: the development of new knowledge “as people engage in a process of reflection related to real problems and issues in their own context” (Saunders, 2011:14). One prominent barrier to the transformative potential of TEL in HE is that “the data needed for informed judgements about the way in which e‐learning provision is actually used in real learning and teaching at module level are very hard to pin down” (Mayes, 2009:52). Therefore, for TEL to be transformational educators need to find effective ways to “pin down” data to inform their judgements. By illuminating the fine detail of self‐evaluative practices when educators implement TEL innovations this study contributes to filling this gap. As Saunders asserts: “[i]f we are to improve the capacity of evaluations to be used (to contribute to

393

Claire Raistrick changes in practice) then we need, … [to] have a robust understanding of what practices are, how might they change and, further identify practices of evaluation design that might encourage use” (2012:424‐425). Saunders’s argument supports this study’s part in extending knowledge on improving self‐evaluative capacity. The SEPT4TEL framework is developed from self‐evaluative practices which educators find useful when making TEL innovations in practice settings – self‐evaluative practices which educators use to pin down data to inform their judgements. In the study on which this paper is based (Raistrick, 2013) significant use of quotations, alongside an analytical commentary make the data handling processes transparent. Unfortunately there is not space in this paper to provide this detail. I focus on providing: a brief explanation of the research strategy; an overview of relevant perspectives: social practice, TEL in HE, and, evaluation and self‐evaluative practices in HE; before commenting on evidence of self‐evaluative practices and sharing the SEPT4TEL framework.

2. Research strategy In this qualitative study seven educators share the story of how they each make and evaluate a TEL innovation in an HE context during the early 2010s. Their evaluations are undertaken as part of a postgraduate award in e‐ learning in academic and professional practice at an English university (I am not involved with teaching this award). Co‐construction is sought using dialogical conversation(s) (DC(s)) to generate and interpret the data. DC is an open, spontaneous approach to exploring situations (Knight & Saunders, 1999). The aim of DC is to seek meaning in the midst of reflexive thinking and mutual construction, assisted by reflexivity and using “challenge, clarification and counter‐argument” (Knight & Saunders, 1999:148‐149). Participants are encouraged to reflect – to release (“unfreezing”) and re‐capture (“freezing”) (Lewin, 1951:228) actions and thinking. Thinking on DCs suggests they are not entirely unstructured, it is more that their structure responds to “predispositions and intents” and they are used as a “personalized instrument”, rather than subjecting participants to a predetermined set of questions (Kushner, 2000:83). Indeed, the pre‐existing world‐views of both the researcher and participant inevitably influence such “predispositions and intents”. Co‐construction increases the prominence of participants’ voices and helps to level my influence as researcher. Next, RUFDATA (Saunders, 2000) (an established evaluation tool) which is recognised as helpful in capturing knowledge‐based evaluative practices (Saunders et al., 2011) is used as a back‐up; either prompting generation of data on self‐evaluation or providing reassurance that the DC has already captured pertinent data. Previous use of RUFDATA to guide educators developing evaluation plans found its structure did not “impose a particular view of the world” (Asensio et al., 2006:6). Helpfully, its neutral framework offers “an accelerated induction to key aspects of evaluation design” (Saunders, 2011:16). Moreover, its seven reflexive questions support the reflective processes routinely recommended as a professional response to educators’ practice (e.g. Schön, 1987; Mayes, 2009). Nevertheless, what we co‐construct about reality remains partial, situated and provisional. Our co‐construction is also value‐laden – permeated by my values and those of participants.

3. Social practice Social practice contains “patterns of bodily behaviour” and “routinized ways of understanding, knowing how and desiring” (Reckwitz, 2002:250). Daily life is defined by these “recurrent, often unconsidered, sets of behaviours … shaped by knowledge resources on which actors routinely draw. … tacit and informal as well as more explicit knowledge areas and skills” (Saunders, 2012:426). Such habitual behaviours become available to actors during social interactions like making and evaluating a TEL innovation. These acts may be profoundly potent showing how “practices” or “clusters of behaviours” signify “ways of ‘thinking and doing' associated with evaluation use” (Saunders, 2012:426). Ultimately this study presents typologies of self‐evaluative practices which exemplify Reckwitz's (2002) portrayal of people as carriers of practice within their social mêlée. So, educators’ social practice becomes apparent through the self‐evaluative practices they actually use in their real‐worlds. As participants socially construct meaning based on real‐life experience they become “suspended in webs of meaning they themselves have spun” (Geertz, 2000:17, after Weber). In this landscape I seek knowledge of the culture of self‐evaluative practices through “thick description” or alternate “horizon[s]” (Geertz, 2000:17) which once seen make everything else within our sight somewhat different.

394

Claire Raistrick

4. Technology enhanced learning in contemporary higher education As technology develops educators use of it remains open and the emphasis is shifted onto “e‐pedagogy – the role to be played by technology in shaping real learning” (Mayes, 2009:52). Institutional investment in technological infrastructure is echoed in job descriptions requiring educators to engage with TEL (meaning use of computers and other devices, digital formats, and the internet regarding learning) and inevitably, effective use of TEL requires educators to be empowered to use appropriate (maybe new) practices and perhaps to change well‐established ways of working. Arguably, “more effective leadership, at all levels, is required to exploit this infrastructure” (Cooke, 2008:3). The change implicit in this ongoing turn to TEL prioritises research on the worth of technological innovations. There is dubious value surely if the technology does not enhance learning? Questions abound, how might educators: establish whether their TEL innovations work; or, implement change processes regarding an alternative learning paradigm; and what effect(s) do changes have on the varying contexts of educators, learners and HE institutions? Issues include barriers regarding the technologies and pedagogies (Goodfellow & Lamy, 2009) and, arguments over the existence of an e‐learning singularity paradigm (Parchoma, 2010), for example. Notwithstanding such questions and issues, the transformative potential of TEL in HE is embedded in a landmark publication providing a “snapshot” of “thinking about the impact of technology” on teaching and learning in HE (Mayes et al., 2009:2). “Massive (and disruptive) technological change” nestles alongside a former vice‐chancellor’s concern about “whether innovation is being embraced quickly enough … [has] reached a [sufficient] scale … and whether there is any way we can bring more hands to the wheel” (Gourley, 2008:1‐2). This challenging contemporary landscape is set to test educators’ abilities to enhance technological proficiency, contemplate new pedagogies and operate in increasingly diverse cultural contexts. The ongoing need to pursue new knowledge regarding use of TEL makes educators’ preparedness for its use particularly relevant. Hence this study’s consideration of self‐evaluative practices and whether better knowledge of them might be an effective way to “bring more hands to the wheel” (Gourley, 2008:1).

5. Evaluation and self‐evaluative practices in higher education Evaluation is used in different contexts and in various guises, including by individuals appraising their everyday life. This paper uses the term evaluation to mean purposeful activity in the sense of Guba and Lincoln’s conception of “disciplined inquiry” (2001:1). Furthermore, though Guba and Lincoln “argue that there is no “right” way to define evaluation” (emphasis in original) (1989:21), Weiss nevertheless provides a useful working definition when describing “evaluation" as “an elastic word that stretches to cover judgements of many kinds” (1972:1 in Clarke, 1999:1). Characteristically, evaluative judgements assign value, merit or worth and form part of social practice with reflective practice being an aspect of a professional response to change: The attribution of value and worth through judgements on what is professionally useful, rewarding or what works is part of social practice that can form the basis of such a reflective culture (Saunders, 2011:15). As well as seeing such “processes of judgement” as “profoundly evaluative” Saunders suggests that “[n]ew knowledge is developed as people engage in a process of reflection related to real problems and issues in their own context” (2011:14‐15). This notion of what is useful is a facet of social practice culture and “evaluative practices which work with social realities can offer powerful support for change” (Bamber, 2011a:160). In the 1980s evaluation was seen as an “intuitive and private” activity (Bamber, 2011b:165); yet it has come to be associated with accountability discourses such as Key Information Sets, quality issues and the student voice. Self‐evaluative practice is not, seemingly, particularly prominent in HE as little is written to indicate what it looks like. Nonetheless, contemporary self‐evaluative practices in HE have been profiled in four chapters of Reconceptualising Evaluation in Higher Education (Saunders et al., 2011), though these chapters do not consider self‐evaluation of TEL innovations. Elsewhere, Curtis highlights self‐evaluative practices as problematic learning requiring work and claims that “[s]elf‐evaluative practices will not emerge like a phoenix from the ashes” (1994:43). Meanwhile, as Saunders reflects, discourse associated with becoming “more ‘efficient and effective’ puts constant pressure on [educators] to review and evaluate what they do, how they do it, and how they could do it better” (2011:16). There is therefore a need for universities, as Curtis makes clear when speaking of quality appraisal, to “learn how to provide more effectively for their own leaning at all

395

Claire Raistrick levels” (1994:46). It is possible however, as it is recognised that learning occurs by doing (e.g. Eraut, 2004, 2008), that educators can learn to self‐evaluate by doing self‐evaluation, as well as learning from their self‐ evaluative outputs. Evaluative practices can apparently create “provisional stabilities” in situations which are complex, messy and uncertain (Saunders et al., 2005:47) so perhaps provisional stabilities can help educators change from one way of teaching or learning to a newer way? In highlighting how educators do “identify and check the assumptions behind their practice and experiment creatively with approaches to evaluation which they themselves have evolved”, Bamber details six tenets of self‐evaluative practices (2011c:193):

Self‐evaluative practices are self‐driven

Agency is not straightforward

Discretion does not mean free‐for‐all

What is evaluated goes beyond the project

Emerging outcomes are worked with, not against

Self‐evaluative practices are a vital part of change efforts

Though not specifically referring to TEL Bamber’s tenets establish knowledge about self‐evaluative practices – “participatory or bottom‐up developmental approaches to evaluation” (Saunders, 2011:13).

6. Evidence of self‐evaluative practices I found that the object of this research, self‐evaluative practices, does exist as part of educators’ day‐to‐day professional practices. Furthermore, educators in this study adopted a “questioning and analytical stance on the nature and pedagogical purpose” (Mayes, 2009:52) of their TEL innovations. This paper does not include quotations from the raw data though participants’ stories did communicate their actions and uncover aspects of their reasoning and sense‐making. Stories I repeatedly considered before assigning themes using the seven RUFDATA categories: Themes relating to each RUFDATA category emerge emphasising the ubiquity of this evaluatory framework. Prominent characteristics of participants’ self‐evaluative practices are iterative, questioning, responsive acts involving stakeholders and reflexivity. Participants connect with the entity being evaluated so that their acts and actions (repetitive nudges) make sense to them as they weave an autobiography of self‐evaluative practices. Thus participants unfreeze and refreeze (Lewin, 1951) what they know and where they are – moving from one provisional state to the next (Raistrick, 2013:215). These provisional stabilities act as intermittent anchorage for the self‐evaluatory process whilst the educator sets their sights on the next provisional endpoint of their journey. Thus their evaluation moves on – evaluative creep occurs. Evaluative creep is transformational – it characterises the effect of self‐evaluative practices on change processes. Evaluative creep is brought about by recurrent behaviours and is a way of coming to know – of realising and sustaining movement towards a provisional endpoint (Raistrick, 2013:193). Ultimately, a picture emerges positioning self‐evaluation as a process: “an organic, consistent, set of behaviours applied to undertaking professional practice” (Raistrick, 2013:174). Regarding Bamber’s tenets of self‐evaluative practices (2011c), this study confirms such practices as: [S]elf‐driven acts in highly contextualised evaluative landscapes where self‐evaluators work with (not against) emerging outcomes. Similarly, agency is not as straightforward as the term self‐ evaluation might imply because evaluative outcomes are woven by drawing together threads from various stakeholders (e.g. educators, their colleagues, and learners) and their respective perspectives (Raistrick, 2013:175). Furthermore, self‐evaluation helps participants “make wiser decisions” (Weiss, 1988:5) and they value this wisdom. There is however, a fundamental difference between the self‐evaluations informing this study and Weiss’s view. Weiss suggests that post‐evaluation “[t]hings usually seem to go along much as they would have gone if the evaluation had never been done” (1988:5). On the contrary, in this study self‐evaluative practices are a vehicle for significant change processes. Thus change is at the heart of self‐evaluation. This corresponds,

396

Claire Raistrick as I highlighted earlier, with the sixth of Bamber’s tenets, namely, that self‐evaluative practices “are a vital part of change efforts” (2011c:193). Additionally, four significant overarching features of the social practice of self‐evaluation are identified: First, the vitality endemic in authentic self‐evaluation is grounded in participants’ drive to enhance the learner experience and improve the status quo. Participants are motivated and want to learn more – the self‐evaluative practices they employ produce rich knowledge even if they do have to compromise or make do with knowledge gleaned in the messy, muddy swamp of real‐ world practice. Second, these educators reach many staging posts along the journey that is their self‐evaluation: a journey peppered with repeated actions and interspersed with recurrent reflexivity. A process they value, explicitly and, as evidenced by their ongoing engagement with self‐evaluation, implicitly. In responding to tensions strong personal characteristics pull them onwards, including enthusiasm, tenacity and perseverance. The absence of a stable state is pervasive. Provisionality is endemic. Regardless, the systemic process of questioning their TEL innovations is valued; even if responding to the contingencies of the moment is more apparent than a planned approach. Third, incremental progress is interjected with evaluative moments, revealing: surprises, interest and insights; leading to new knowledge. Self‐evaluative thinking legitimises change processes, justifying alternative realities, and approving new provisional stabilities. The educator is central to this process. Embedded within the action is the question of how educators come to know. Last, one key way in which self‐evaluation is rendered authentic is by incessant reflexivity: looking backwards to facilitate moving forwards (Raistrick, 2013:192). Next I present the SEPT4TEL framework.

7. Typologies of self‐evaluative practices The seven categories of the RUFDATA framework (Saunders, 2000) have been used previously to depict evaluative practice in HE (Saunders et al., 2011) and are consequently adopted here to present self‐evaluative practices via this study’s SEPT4TEL framework. Thus, the SEPT4TEL framework (Raistrick, 2013) consists of seven typologies, each of which offers a series of considerations synthesised from the self‐evaluative practices of educators who participated in this study. Next, I detail each of the seven RUFDATA questions, providing a characteristic response to indicate the key intent of these educators when making their TEL innovations. I also list the considerations which this study identifies as relevant to educators’ self‐evaluative practises when making TEL innovations. These considerations may provide guidance for other educators undertaking self‐ evaluation of TEL innovations. What are my Reasons and Purposes for evaluation? To benefit student learning. Considerations:

Establish the worth of an innovation.

Improve pedagogical aspects of a TEL innovation, by undertaking deliberate evaluative acts.

Realise and sustain evaluative creep towards continuously provisional endpoints.

Achieve professional development.

What will be my Uses of my evaluation? To inform change processes. Considerations:

Reveal new knowledge both to focus and inform evaluative acts at various stages of the implementation cycle, i.e. the identification, development, and assessment of the effect, of the TEL innovation.

Gain clarity regarding what aspect(s) of the TEL innovation is to be changed / improved.

Enable a responsive approach which increases educators’ confidence and competence, regarding implementation of the innovation and outputs from the evaluation.

What will be the Focus (foci) for my evaluation? The effect(s) of the TEL innovation.

397

Claire Raistrick Considerations:

Be aware of the TEL innovation’s effect(s) at different stages of its implementation.

Determine what aspect(s) of the TEL innovation interest you, e.g. interaction, learning gains, educator gains, fit with other learning or lifestyle events.

Collect specific evaluative data from sources which include appropriate metadata and diverse stakeholders.

Identify forms of data that are sufficient to convince decision‐makers (including the self‐evaluator).

Record data on iterative changes and improvements and the way the process is kept live.

Identify indicators of success and problematic gaps affecting the integrity of the innovation.

What will be my Data and Evidence for my evaluation? Accessible, straightforward, manageable data and evidence. Considerations:

Recognise, provoke and facilitate opportunities to generate, capture and analyse physical and non‐ physical forms of data and evidence, using these to inform decision‐making as part of the change process.

Perform responsive, organic and recurrent acts, marking progress by multiple provisional endpoints.

Encourage and welcome wide participation, via provision of candid feedback, from diverse sources (stakeholders), including reflexively.

Realise that generating new knowledge has a potent effect on sense‐making.

Pull together multiple strands to construct meaning.

Accept that data and evidence needs to be sufficient for its current purpose and no more.

Exhibit tenacious perseverance in pursuing new knowledge with the practical potential to influence change processes.

Be receptive to both strategic and serendipitous opportunities.

Repeatedly refine the approach to evaluation and the innovation.

Identify what is beyond the remit of the current stage of evaluation and adjust the evaluative process (and TEL innovation) accordingly.

Who will be the Audience for my evaluation? Learners, close colleagues and other educators within and beyond the institution. Considerations:

Accept the centrality of yourself as evaluator.

Engage the attention of others with the potential to influence development of the innovation, including current and future target groups and sub‐groups, i.e. users.

Show, promote and share evaluative knowledge (outputs) interactionally, rather than only by presentation or distribution, to influence other educators’ practice (use and engagement).

Make connections with wider issues or questions about which you are curious.

What will be the Timing for my evaluation? Within and beyond the confines of the award submission date (and then other provisional endpoints). Considerations:

Select a project that is important to you to increase the likelihood of having sufficient time.

Identify an initial provisional endpoint to work towards.

Remain sensitive to temporal effects and continually adjust processes (nudge) to accomplish what is achievable; thus reaching multiple, revised, provisional endpoints.

Sustain an iterative, continuous cycle of planning, evaluation, reflexivity, and adaptation.

398

Claire Raistrick

Respond in a timely way to evaluative knowledge, including incorporating cyclic institutional events.

Build‐in staging posts to provide space and time for reflexive processes, e.g. dialogical conversation.

Remain attuned to temporal issues (and other barriers) to minimise restraint on momentum.

Who should be the Agency conducting the evaluation? Myself, as the educator making the TEL innovation, with input from interested outsiders. Considerations:

Take responsibility yourself, for:

Progress, including accessing resources.

Involving stakeholders.

Potential bias.

Recognise that external entities (person or structure, e.g. RUFDATA framework) can provide valuable input.

The series of considerations for self‐evaluative practice represented in the SEPT4TEL framework exemplify Reckwitz's (2002) description of people as carriers of practice within their social mêlée, providing detailed evidence of self‐evaluative practices. These seven typologies present distinctive new knowledge on considerations for self‐evaluative practice; knowledge emerging from co‐constructed data created by educators undertaking self‐evaluation of a TEL innovation. These educators take responsibility for a change process by engaging with and becoming empowered to acquire two sets of new skills: regarding the TEL innovation itself and the self‐evaluative practices they use. Consequently, this SEPT4TEL framework is particularly recommended to educators trying to implement bottom‐up development of TEL innovations. SEPT4TEL is not however seen as a single right way to undertake self‐evaluation – it simply offers guidance to practitioners. Each use is likely to be idiosyncratic and to emerge out of an individual self‐evaluator’s context, interests, knowledge (tacit and explicit), practices and experience. Perhaps, even reflecting their discipline? Furthermore, SEPT4TEL is potentially a multi‐disciplinary or even inter‐disciplinary, methodologically neutral tool, despite being situated within the inherently reflexive domain of self‐evaluation.

8. Conclusion This research has typologised features of self‐evaluative practices and the resulting SEPT4TEL framework provides guidance to assist educators undertaking self‐evaluation of TEL innovations. SEPT4TEL promotes an authentic and systemised use of self‐evaluation to support educators’ learning about their professional practice – in evaluative cultures where learning is fundamental. As an approach SEPT4TEL has the potential to support educators’ attempts to work on new ways to become more productive or efficient, to improve the student learning experience and to enhance their own intrinsic satisfaction – all in relation to TEL. Repeatedly refreshing thinking about the seven self‐evaluative categories (prompted by RUFDATA) as a TEL project progresses might assist educators to pre‐empt the project’s impact and incrementally increase the worth of their self‐evaluation. Equally, staff with institutional responsibilities for planning processes of organisational development may wish to consider how policies, procedures and other objectives might be informed by promoting this new knowledge. Thus the SEPT4TEL framework has the potential to influence mainstreaming of TEL, particularly if it does so in ways which are pedagogically transformational. The next step is to invite educators, individually or in groups, to use the SEPT4TEL framework to assess the value it brings to their self‐ evaluation of TEL innovations. This paper is based on my doctoral thesis submitted to Lancaster University in part fulfilment of the PhD in e‐ research and technology enhanced learning (Raistrick, 2013).

References Asensio, M, Hodgson, V & Saunders, M (2006) Developing an inclusive approach to the evaluation of networked learning: The ELAC experience. In: Banks, S, Hodgson, V, Jones, C, Kemp, B, McConnell, D & Smith, C Eds. Networked Learning 2006. Lancaster, UK. Lancaster: Lancaster University: 1‐8. Bamber, V (2011a) Evaluative practice and outcomes: Issues at the institutional level. In: Saunders, M, Trowler, P & Bamber, V Eds. Reconceptualising evaluation in higher education. Maidenhead: Open University Press: 156‐161. Bamber, V (2011b) Self‐evaluative practices: Diversity and power. In: Saunders, M, Trowler, P & Bamber, V Eds. Reconceptualising evaluation in higher education. Maidenhead: Open University Press: 165‐170.

399

Claire Raistrick Bamber, V (2011c) Evaluative practice and outcomes: Issues at the self‐evaluative level. In: Saunders, M, Trowler, P & Bamber, V Eds. Reconceptualising evaluation in higher education. Maidenhead: Open University Press: 193‐199. Clarke, A (1999) Evaluation research. London: Sage. Cooke, R. (2008) On‐line innovation in higher education. Submission to the Rt Hon John Denham MP, Secretary of State for innovation, universities and skills. [Report] Available from: http://webarchive.nationalarchives.gov.uk/+/http://www.dius.gov.uk/policy/documents/online_innovation_in_he_1 31008.pdf (Accessed: 13 March 2013). Curtis, S (1994) Higher education in Australia: When organisations for learning need to become learning organisations. Journal of Institutional Research in Australia, 3 (1): 39‐50. Eraut, M (2004) Informal learning in the workplace. Studies in Continuing Education, 26 (2): 247‐273. Eraut, M. (2008) How professionals learn through work. 1‐29. [Online draft working paper] Available from: http://surreyprofessionaltraining.pbworks.com/f/How+Professionals+Learn+through+Work.pdf (Accessed: 13 March 2013). Geertz, C (2000) Available light. Princeton, NJ: Princeton University Press. Goodfellow, R & Lamy, M‐N (2009) Introduction: A frame for the discussion of learning cultures. In: Goodfellow, R & Lamy, M‐N Eds. Learning cultures in online education. Continuum studies in education. London: Continuum Books: 1‐14. Gourley, B M. (2008) Higher education for a digital age. Plenary at the Association of Commonwealth University Conference of Executive Heads: Dazzling technologies: seismic shifts in higher education in a fast‐changing and unequal world [Online] Available from: http://hyderabad2008.acu.ac.uk/presentations/Brenda_Gourley.doc (Accessed: 25 April 2013). Guba, E G & Lincoln, Y S (1989) Fourth generation evaluation. London: Sage Publications. Guba, E G & Lincoln, Y S. (2001) Guidelines and checklist for constructivist (a.k.a. Fourth generation) evaluation. [Online] Available from: http://www.wmich.edu/evalctr/archive_checklists/constructivisteval.pdf (Accessed: 13 March 2013). Knight, P & Saunders, M (1999) Understanding teachers' professional cultures through interview: A constructivist approach. Evaluation and Research in Education, 13 (3): 144‐156. Kushner, S (2000) Personalising evaluation. London: Sage. Lewin, K (1951) Field theory in social science: Selected theoretical papers. New York: Harper & Brothers. Mayes, T (2009) All in the mind: Programmes for the development of technology‐enhanced learning in higher education. In: Mayes, T, Morrison, D, Mellar, H, Bullen, P & Oliver, M Eds. Transforming higher education through technology‐ enhanced learning. York: HEA: 46‐57. Mayes, T, Morrison, D, Mellar, H, Bullen, P & Oliver, M Eds. (2009) Transforming higher education through technology‐ enhanced learning. York: HEA. Parchoma, G (2010) Toward diversity in researching teaching and technology philosophies‐in‐practice in e‐learning communities. In: Daniel, B Ed. Handbook of research on methods and techniques for studying virtual communites: Pagadigms and phenomena. Hershey, PA: IGI Global: 61‐86. Raistrick, C (2013) Educators’ self‐evaluative practices when making technology enhanced learning innovations in higher education. Doctor of Philosophy, Lancaster University, UK. Reckwitz, A (2002) Toward a theory of social practices: A development in culturalist theorizing. European Journal of Social Theory, 5 (2): 243‐263. Saunders, M (2000) Beginning an evaluation with RUFDATA: Theorising a practical approach to evaluation planning. Evaluation, 6 (1): 7‐21. Saunders, M (2011) Setting the scene: The four domains of evaluative practice in higher education. In: Saunders, M, Trowler, P & Bamber, V Eds. Reconceptualising evaluation in higher education. Maidenhead: Open University Press: 1‐17. Saunders, M (2012) The use and usability of evaluation outputs: A social practice approach. Evaluation, 18 (4): 421‐436. Saunders, M, Charlier, B & Bonamy, J (2005) Using evaluation to create 'provisional stabilities': Bridging innovation in higher education change processes. Evaluation, 11 (1): 37‐54. Saunders, M, Trowler, P & Bamber, V Eds. (2011) Reconceptualising evaluation in higher education. Maidenhead: Open University Press. Schön, D (1987) Educating the reflective practitioner. San Francisco, CA: Jossey Bass. Weiss, C H (1988) Evaluation for decisions: Is anybody there? does anybody care? The American Journal of Evaluation, 9 (1): 5‐19.

400

A Reality Check on Student Mobile Adoption and Content Creation in Resource‐Constrained Environments Patient Rambe and Liezel Nel Department of Computer Science and Informatics, University of the Free State, South Africa pjoerambe@gmail.com liezel@ufs.ac.za Abstract: Although the African continent’s adoption of mobile devices and access to networked resources are often euphorically conceived as a mobile revolution and a technological miracle respectively, the South African mobile technology landscape does not perfectly fit these hyped theorisations. Notwithstanding the impressive uptake of mobile technologies, differential adoption of mobile phones and gradations of access to educational resources persistently plague South African students’ ownership of mobile devices and use of online resources. To debunk the aforementioned myth about the mobile revolution and universal access to online educational resources, Keller’s (1984, 1987) ARCS model and a departmental climate survey were drawn upon to unravel Computer Science and Informatics students’ ownership of mobile devices and the extent of their participation in the creation of online content. Findings suggest that notwithstanding the moderately high ownership of low‐end mobile phones, smartphones, and laptop computers, the ownership of other intelligent devices (PDAs, tablets, netbooks and e‐readers) and digital recording devices (digital cameras, audio and video players) is still highly differentiated and constitutes an emerging social phenomenon. Student modest adoption of social networking technologies (like Facebook and Twitter) for content creation is juxtaposed by a disturbing non‐use of other Web 2.0 technologies such as blogs, wikis, podcasts, Google documents and mobile instant messaging applications. The implications of these dynamics for pedagogy include the need for educators to demonstrate the relevance of using mobile technologies for student meaningful learning, broaden student knowledge and confidence in the academic appropriation of emerging technologies and enhance their academic satisfaction with new technologies. This could be enabled by using pedagogical strategies that directly draw on particular emerging technologies to ensure student productive adoption of these technologies. Keywords: mobile revolution, mobile adoption, online educational resources, online content creation, technological satisfaction

1. Introduction The South African experience of the appropriation of mobile devices and ubiquitous access to networked resources has dominated mainstream literature (Rao, 2011; Rawlinson, 2011, Goldstuck, 2012; Inter‐gate Immigration, 2012). The explosive uptake of mobile phones and correspondingly impressive internet penetration in South Africa has reinforced the euphoric faith in an African mobile revolution and technological miracle respectively. Literature documents that the country’s mobile landscape has considerably surpassed that of stand‐alone desktop computers. As at October 16, 2012, there were approximately 29 million mobile phone users in South Africa compared to 6 million users of desktop computers (Inter‐gate Immigration, 2012). Similarly, Goldstuck (2012) highlights that 7.9 million South Africans accessed the Internet on their cell phones and the country’s smart phone user projections surged from 8.5 million in 2011 to 11 million users in 2012. Nicolson (2011) predicts that the mobile phone will become the main mode of “information on the move” for millions of Africans in general and South Africans in particular. This sporadic rise in the appropriation of mobile devices reinforces the self‐fulfilling prophecy of Africa becoming the first post‐PC continent, due the paucity of laptops, iPads and Kindles among most Africans (Wanjiku, 2011). A “perfect storm” has also been predicted with regard to student access, creation, curation and repurposing of web‐based content. This enthusiastic projection is reinforced by surging broadband subscriptions and the ubiquity of mobile data. The World Wide Worx (2012) study demonstrates that South African broadband subscriptions grew from 3.6 million in 2010 to an estimated 8.2 million by 2012 - a 128% growth. In th 2009, South Africa ranked a respectable 6 in the global Top 10 for mobile internet usage, ahead of both the US (7th) and the UK (9th) (Czerniewicz, 2009). This “perfect storm” of mobile data or data “tsunami” constitutes a technology revolution involving the overflow of usable data across networked platforms. Voices of South Africa (2013) reports that Telkom, South Africa's telecoms operator’s decision to provide free Internet access to mobile phone subscribers who cannot afford data costs has further broadened public access to mobile data. This paper argues that notwithstanding the hype about high mobile phone adoption and ubiquity of online data, the reality is that high‐end mobile phone ownership varies significantly and access to these gadgets

401

Patient Rambe and Liezel Nel remains differentiated across various student groups at university. Literature suggests that although mobile phone ownership is considerably high at South African universities (about 98%) (Centre for Educational Technology, 2010), smart phone ownership cannot be assumed to be equal and balanced (Czerniewicz, 2009). Recent studies also confirm the prevalence of digital strangers in South African higher education and student asymmetrical access to educational technologies on and off campus (Czerniewicz, 2009, Czerniewicz and Brown, 2013). Digital strangers are “students lacking both experience and opportunities, who had barely used a computer and who did not have easy access to technology off campus” (Czerniewicz and Brown, 2013). Transcending the binary of natives and strangers that tends to restrict online engagement to seniority and ICT background, this work adopts the concepts of ‘visitors’ and ‘residents.’ The visitors and residents continuum accounts for people behaving in different ways when using technology, depending on their motivation and context (White and Le Cornu, 2011). The paper also argues that social differentiation of student access to networked educational resources persists at South African universities due to slow internet connections, erratic connectivity, dependence on institutional networks for broadband connectivity and varied access to web‐enabled mobile devices. Peyper (2013) highlights that although mobile phones remain the dominant technology for voice and data communication among South African users, these users often rely on Facebook Zero and WhatsApp to bypass the expensive Short Message Service (SMS) rates of mobile phone networks. High tariffs on voice, SMSs and bandwidth‐intensive multimedia content constrain mobile phone centric student access and use of online content. Mindful of differential access to high‐end networked devices and asymmetrical access to internet‐based content, the paper employs John Keller’s (1987) ARCS model and a survey on Computer Science and Informatics (CSI) students at a South African university’s ownership of mobile devices and the extent of their participation in the creation of online content to answer the following research questions:

What are the preferences and extent of mobile device and social technology (digital cameras, audio and video players) ownership of CSI students at this institution?

What environmental and personal considerations influence such access and usage?

How frequent do CSI students use Web 2.0 (blogs, wikis, podcasts, Google documents and MIM applications) and social technologies (Facebook, Twitter) to participate in online creation of content?

To what extent does student use of online content via social networking and Web 2.0 technologies fit the “digital resident” identity?

2. Literature review Rawlinson (2011) reports that in February 2011, South Africa ranked an impressive 5th in the world for mobile data usage. This reinforces the unsubstantiated claim about universal access to mobile data and ubiquity of mobile devices. Since mobile data usage is unproblematically conceived as a young adult phenomenon, the aforementioned claim entrenches perceptions of the “digital natives” (Prensky, 2001) identity of university learners. Digital natives are young adults who, through their growing up with and wide exposure to technology, have internalised and mastered online content generation, digital curation and repurposing of data. They have mastered technology‐mediated practices, possess the skills, abilities and competences to effectively use networked technologies and function in diverse technology‐rich learning contexts. It is critical to highlight that with the advent of the new millennium where all humans (in the developed world) have now grown up in the era of digital technology, the distinction between digital natives and digital immigrants has become less relevant, as the concept of digital wisdom takes over (Prensky, 2009). For Prensky (2009) digital wisdom refers both to wisdom arising from the use of digital technology to access cognitive power beyond human innate capacity and to wisdom in the prudent use of technology to enhance human capabilities. In reality, however, with the skewed disparities in access to technologies that persist in the developing world, the concept of digital visitors and residents will continue to take centre stage in the Global South, especially in Africa. For instance, the aforementioned commendable statistics that Rawlinson (2011) reports on obscure the realities about the high social differentiation in the ownership of high‐end phone (smart phones, PDAs, tablets, netbooks and e‐readers) ownership and student appropriation of mobile content. There is convergence of opinion on the differentiation of mobile phone ownership, worsening of the networked divide and the prevalence of “digital strangers” (Czerniewicz, 2009; Ng’ambi, 2011; Czerniewicz and

402

Patient Rambe and Liezel Nel Brown, 2013) in South African technology‐saturated environments. Rambe and Bere (in press) highlight that educators’ use of WhatsApp, a mobile instant messaging (MIM) service for sharing networked resources among Information Technology students was often hampered by disadvantaged students’ lack of access to smart phones. They report that the networked divide was further accentuated by some mature, married students’ resentment of WhatsApp for after hour consultations which merged academic and family life serendipitously. Similarly, Czerniewicz (2009) reports on Centre for Educational Technology (CET) (2004, 2007) studies on access and use of mobile phones among over 10 000 students from over 12 South African universities. She affirms differentiated access to networked technologies and highlights that while on campus access was generally equal and equivalent; off campus access varied and was unequal. A significant 22 per cent of those researched still lacked experience and opportunity to networked computers and other devices off campus. This finding reinforces the view that the networked divide might be widening. With regard to connectivity, Ng’ambi (2011) suggests that off campus South African students had limited or no access to broadband connectivity notwithstanding a sound mobile communication infrastructure. As such, accessing Internet resources on the mobile phones was expensive let alone voice calls.

3. Theoretical framework John Keller’s (1984, 1987a) ARCS model of motivation constitutes an intervention for finding effective ways of understanding the major influences on learning motivation and solving problems associated with it (Keller 1987a). The ARCS model comprises four mutually interdependent variables ‐ Attention (interest), Relevance, Confidence (expectancy for success) and Satisfaction ‐ which basically connect the effort technology users expend to the performance of particular activities and consequences. Attention encapsulates strategies for capturing the interest of the learners and stimulating their curiosity to learn (Keller, 1987b). Keller (1987c) highlights that attention involves strategies for capturing students’ interest (perceptual arousal), stimulating an attitude of inquiry (inquiry arousal) and retaining attention (variability). Although Keller’s model is an instructional macro‐model for motivational pedagogical designs, it has some resonance with student access and appropriation of mobile devices. Student access and productive use of particular mobile devices are potentially a function of their perceptions and educators’ expression of the social and cultural significance of technologies (attention). Therefore, recruiting and retaining student’s commitment to use mobile technologies (attention) could happen through: (1) Affirming the general communicative affordances and networking possibilities of particular mobile devices without necessarily emphasising their educational qualities; (2) Exposing diverse applications, additional functionalities and unexpected affordances of these devices; and (3) Diversifying the social and interactive uses of the technology. Relevance underlies meeting the personal needs or goals of the learner to effect a positive attitude (Keller, 1987b). It entails strategies for meeting students’ needs (goal orientation), strategies and conditions under which learners acquire appropriate choices and responsibilities (motive matching) and connecting instruction to learner’s experiences (familiarity) (Keller, 1987c). While relevance focuses on strongly anchoring instruction in learners’ academic aspirations, in mobile adoption this could underpin students’ personal evaluation of the compatibility of mobile technologies to the realisation of their educational needs and goals. As such, students’ effective adoption of mobile devices for academic purposes depends on the perceived capacity of these technologies to meet their learning needs and promote the enactment of individual agency in pursuit of their academic goals. Confidence entails helping the learners believe that they will succeed and control their success (Keller, 1987b). This underpins strategies for building students’ positive expectation for success (learning requirements), ensuring the learning experience supports their belief in their competences (success opportunities), and guaranteeing the learners’ success based on their efforts and abilities (personal control) (Keller, 1987c). Student ownership, use and confidence in a mobile device potentially depend on their expectations about the technology’s contribution to fulfilling their course requirements (good grades, securing a qualification). This also encapsulates the extent to which the technology perceptibly supports a positive learning experience that enhances students’ confidence in their academic potential.

403

Patient Rambe and Liezel Nel Satisfaction involves reinforcing accomplishment with rewards (internal or external) (Keller, 1987b). This necessitates provision of authentic opportunities for learners to use newly acquired knowledge or skills (natural consequences), provision of reinforcements to learners’ successes (positive consequences), and anchoring positive feelings in learners about their accomplishments (equity) (Keller, 1987c). This might imply mobile users’ derivation of personal satisfaction from the mobile technology as a consequence of its capacity to enhance the broader application of acquired knowledge. Lastly, the ARCS model also acknowledges the significance of the social context in shaping learners’ behaviour such as their decision to adopt and use particular technologies. After careful consideration of the ARCS model and various related mobile learning concepts (as discussed above), the researchers created an ARCS rubric for mobile learning (see Table 1). In this rubric the various ARCS concepts are placed against mobile phone affordances. Table 1: Rubric for applying ARCS model to mobile learning ARCS Concept Attention Relevance

Confidence

Satisfaction Context

Mobile phone affordances Affirming communicative affordances and networking possibilities of a mobile device without necessarily emphasising its educational qualities (interest) Exposing diverse functionalities and applications on mobile devices, including unexpected affordances (variability). Diversifying social and interactive uses of mobile technology (inquiry arousal). Student evaluation of the compatibility of mobile technologies with the realisation of their educational needs and goals (e.g. academic networking and social learning, productive engagements, collaboration) (goal orientation). Mobile technology’s conditions and capacity to enact student agency and self‐regulation in pursuit of their academic mandates (e.g. technological relevance to allow self‐awareness of functions and self‐teaching, triability and use across different contexts, capacity to support self‐ paced learning) (motive matching). Mobile device’s contribution to student fulfilment of course requirements (good grades, securing qualifications) (Learning requirements). Mobile devices’ enhancement of positive learning experiences that improve students’ confidence in their academic potential (access to learning resources and knowledgeable peers, productive engagement, build their linguistic competence) (success opportunities). Device supports personal effort and initiative to succeed (user generation of content, user development of applications, extension of user’s academic community) (personal control). Mobile devices’ affordances for the application of acquired knowledge in other courses or disciplines (natural consequences). Provision of sense of personal accomplishment and proficiency in using mobile devices (equity). Facilitating conditions within the social environment that support effective access and adoption of mobile devices.

4. Methodology This study employed a survey approach. The survey approach seeks to gather large scale data from as representative a sample population as possible in order to say with a measure of statistical confidence that certain observed characteristics occur with a degree of regularity (Cohen, Manion and Morrison, 2007). The purpose of this study was to ascertain first (n=60), second (n=35) and third year (n=14) CSI students at a South African university’s preferences and extent of ownership of mobile devices. The study also examined the extent of students’ engagement in online content creation via Web 2.0, social technologies, digital recording devices, the personal and social influences of this use including whether such usage typifies a “digital resident” identity. A survey relies on a questionnaire whose objective “is to obtain facts and opinions about a phenomenon from people which are informed on the particular issue” (Delport and Roestenburg, 2011, p. 186). A semi‐structured questionnaire, which comprised demographic data and Likert scale‐based questions was hosted on the Survey Monkey website. All undergraduate CSI students were invited to participate in the survey and 97 respondents completed the questionnaire.

404

Patient Rambe and Liezel Nel

5. Data analysis Data analysis firstly involved the generation of frequency tables to determine students’ preferences, ownership of mobile devices and their engagement with online content via Web‐based social technologies. The researchers then applied the ARCS rubric for mobile learning (see Table 1) to the survey findings (see Table 2) in order to get a better understanding of how student technology ownership, extent of use and possible motivations for use look through an ARCS Model lens. In Table 2, the findings on student ownership, extent of cell phone use and personal and contextual factors influencing these patterns of use are mapped against the ARCS rubric for mobile learning to provide an integrated analytical framework of mobile learning in resource‐ constrained contexts. Table 2: Student technology ownership, extent of use and possible motivations for use ARCS Model lens Context Attention

Technology owned Ordinary phone

Extent of use

Personal and environmental considerations

Extensive

Context

Smart phone

Extensive

Attention Confidence Context Relevance Satisfaction

Laptop

Extensive

Tablet

Like to own

Low cost, free SIM cards and ubiquity. Device communicative capabilities. Social cohesion and networking capabilities. Substitute for laptops. Network reliability. Option in the absence of laptops. Familiarity and ubiquity. Aesthetic properties. Access to diverse applications. Social interaction possibilities Academic networking possibilities. Social influence from educators and peers. Networked connectivity. Academic networking, collaboration when used with social networking sites, self‐regulation and self‐pacing of learning. Used as an information repository –download content to read offline. Supports a personalised learning environment when used in conjunction with knowledge gateways and platforms. Direct resonance with academia. Employed for note taking. Used for online information searches and problem solving. Exorbitant cost. Contractual obligations. Aesthetic properties. Academic planning, networking and resonance. Self‐organisation and micro management. One stop shop‐communicative. Collaborative affordances.

Context Attention Relevance

6. Presentation and discussion The most widely owned mobile devices were low‐end mobile phones, smart phones and laptops.

6.1 Popular technologies Compared to other mobile technologies, low‐end mobile phones, smart phones and laptops were the most accessible and ubiquitous technologies. Although student also expressed their preferences for other technologies, they had limited access to these technologies (see Table 3). 6.1.1 Context In hierarchical order, the widely accessed and owned mobile devices among students were laptop computers (73.2%), smart phones (71.1%) and mobile phones (55.7%). Several contextual factors (see Table 2) explain this phenomenal adoption including the declining cost of mobile phones and laptops on the South African market with the entrance of new technologies and provision of promotional services by Mobile Network Operators (MNOs). For instance, South African MNOs such as MTN, Cell C and Vodacom have provided their customers with free calls, SMSs and mobile data upon “Top up” (i.e. the purchase of airtime). For instance, MTN’s “Pay as

405

Patient Rambe and Liezel Nel You Go” service and Cell C’s “super charge” service are typical examples. The Voices of Africa (2013) notes that "Free Zone" subscribers to Telkom's 8ta mobile service access the web, Gmail and Google+ without paying for data charges provided they have data‐enabled handsets. Table 3: CSI students’ preference and ownership of mobile devices (n = 97) Mobile device

Don’t know (1)

Personal digital assistant (PDA) Smart phone Mobile phone Audio player Video player Laptop Netbook Digital camera E‐reader Tablet device

17.5% 0.0% 3.1% 3.1% 2.1% 0.0% 1.0% 2.1% 14.4% 2.1%

Don’t own and don’t want (2) 34.0% 4.1% 36.1% 30.9% 28.9% 3.1% 40.2% 16.5% 35.1% 20.6%

Don’t own but would like to (3) 44.3% 24.7% 5.2% 38.1% 50.5% 23.7% 51.5% 47.4% 47.4% 66.0%

Already own (4)

Rating average

4.1% 71.1% 55.7% 27.8% 18.6% 73.2% 7.2% 34.0% 3.1% 11.3%

2.35 3.67 3.13 2.91 2.86 3.70 2.65 3.13 2.39 2.87

Moreover, the high ownership and access to mobile phones and laptop computers can also be attributed to their ubiquity on the South African market. The World Wide Worx’s (2012) Internet study highlights that the explosive growth of smart phones and ordinary phones on the South African market accounts for the accelerated growth of its internet user base of approximately 25%. MNOs and service providers are either selling SIM cards at low prices or imputing their cost in phone prices. Similarly, the purchase price of laptops is sometimes inclusive of SIM cards and 3G modems. These contextual factors account for the high adoption of mobile phones and laptops. For digital residents, their being socialised into these technologies from birth could explain the high adoption particularly in resource‐constrained environments where desktop computers have remained a rare phenomenon. Lastly, reliable connectivity of students’ smart phones and laptops to institutional networks enable students to access all content from learning management systems. 6.1.2 Attention The popularity and preference for low‐end mobile phones and smart phones among students might be attributed to their attention as perceivably non‐intrusive and personalisable technologies that gave students a sense of privacy (Rambe and Bere, in press) compared to institutionally‐provided desktop computers. Their other attention capabilities are their general communicative affordances, synchronous (MIM) and asynchronous capabilities (voice calls and SMSs) and other cost‐saving mechanisms like flashing and “call back” services. Flashing is a free, communicative practice where a mobile user calls a recipient and then abruptly hangs up before the recipient can answer – serving as an indication to the recipient to phone him/her back. The “Please Call Me Back” service is a free MIM service that allows communicants to send messages requesting a fellow mobile phone user to call them back. The aforementioned services are ideal for full‐time students who often have limited airtime to make expensive calls to peers or educators about academic queries. As Litchfield et al (2007) suggest, a major challenge yet to be overcome in mobile communication is the cost of mobile hardware, software, connection and usage charges. Moreover, mobile phones’ social networking capabilities and affordances for psycho‐social support make them perfect complements or substitutes for students with limited access to laptop computers. In the absence of a laptop, Kindle or iPad, the mobile phone is the key to sustained “information on the move” (Nicholson, 2011). Smart phones and modern laptops possess aesthetic qualities as “goods of ostentation”. Given the subtle social influence and consumerist dispositions among students, they may inadvertently compete to secure the latest technological gadgets for impression management and to secure a sense of belonging. Access and ownership of sophisticated gadgets become tools for self‐expression and enactment of attention‐seeking behaviour. 6.1.3 Relevance Smart phones and web‐enabled laptops enable access to flexible information gateways, databases and learning platforms. As such, they support student creation of flexible personalised learning environments for

406

Patient Rambe and Liezel Nel accessing self and peer‐generated content, self‐generated networks and applications. Collectively, these intelligent devices allow academic networking through access to multiple friendship networks and collaboration on learning tasks (goal orientation). Both laptops and smart phones have strong resonance with resource‐constrained environments where students rely on downloading electronic resources from the Web and reading from their devices offline (Ngambi and Rambe, 2008) due to unreliable networks. 6.1.4 Satisfaction The extensive ownership (73.2%) of laptops can be attributed to their academic resonance as essential devices for taking notes, accessing online databases and solving problems.

6.2 Less accessible but popular technologies Some high‐end networked devices like PDAs, e‐readers, tablet devices and digital recording/ playback devices (video players and MP3 players) were among the popular technologies student were keen to own. 6.2.1 Context Although students highlighted their preferences for PDAs, e‐readers, tablet devices and digital recording devices (video players and MP3 players), their limited use might be attributed to the exorbitant cost of these devices. Since most students do not have the financial means to pay in cash for these devices, their only alternative is to buy the device on contract. In order to qualify for such a contract MNOs and service providers require applicants to provide proof of a steady income (in the form of payslips and bank statements) ‐ which most students do not have. These contract requirements can therefore be regarded as a major barrier to student acquisition of these gadgets. 6.2.2 Relevance Although students had no access to the aforementioned devices for various reasons, their preference for these technologies was precipitated by their academic relevance. They regarded these devices as potential information repositories for accessing educational content (e‐Books, Google applications) and a means to access incredible applications for academic planning (calendars), academic networking, self‐organisation and micro‐management (file and document organisation applications, navigation software). For the students, these devices also had strong academic resonance as they rendered a one‐stop communicative and collaborative shop for multiple academic engagements. 6.2.3 Confidence Student choice of high‐end mobile devices was also informed by the opportunities these devices created for boosting confidence in their academic potential through broadening their access to self‐generated resources, self‐identified affinities, and platforms for building their linguistic competence through digital practice (success opportunities).

6.3 Frequency of engagement in online content creation Students’ involvement in the creation of online content was generally low and restricted to social networking. Students reported minimal engagement with online content creation via Facebook commenting (42.3%) and Tweeting (22.7%) on a weekly basis. The majority of students reported their non‐involvement in the following content creation practices: podcasting (89.7%); uploading and sharing videos online (77.3%), writing Google documents (77.3%), blogging (69.1%), creating wikis (68.0%), chatting on a MIM service and MXit (61.9%) (see Table 4). 6.3.1 Attention The lack of a strong culture of technology integration among educators played out in student orientation towards social networking. Their preference for Facebook and Twitter could be connected to attention attributes like the desire for a strong psycho‐social support in the absence of family members and for micro‐ management of their social lives.

407

Patient Rambe and Liezel Nel Table 4: Student frequency of engagement with creation of online content

Content creation

Create or contribute to a blog Create or contribute to a wiki Create a podcast Upload a video (e.g. on You Tube) Upload photos (e.g. on Flicker, Picasa) Write up a Google document Post some comments on Facebook Send a tweet via Twitter Chat on MXit

Never (1)

Two or three times a semester (2)

Once a month (3)

More than once a month (4)

Once a week (5)

More than once a week (6)

Rating average

69.1%

14.4%

6.2%

5.2%

4.1%

1.0%

1.64

68.0%

21.6%

5.2%

4.1%

0.0%

1.0%

1.49

89.7%

4.1%

0.0%

2.1%

1.23

77.3%

14.4%

5.2%

0.0%

1.0%

2.1%

1.39

56.7%

15.5%

14.4%

7.2%

2.1%

4.1%

1.95

77.3%

10.3%

4.1%

3.1%

2.1%

1.51

21.6%

6.2%

10.3%

13.4%

42.3%

4.14

58.8%

5.2%

2.1%

4.1%

7.2%

22.7%

2.64

61.9%

8.2%

2.1%

4.1%

15.5%

2.25

6.3.2 Relevance Educators’ marginal “educational” use of social network services to send announcements, provide supplementary educational resources and do general academic planning explains student involvement in these familiar technologies. The students were, however, least involved in the digital recording/ playback devices least used by educators.

6.4 Evidence of digital resident identity The study demonstrates student access and use of a limited set of first generation mobile technologies (low‐ end mobile phones, smart phones and laptops) and their marginal use of second‐generation devices (e‐ readers, tablets, netbooks and PDAs) and digital recording/ playback devices (video cameras, audio players and video players). More so, there was scant evidence to support the digital resident identity judging from students’ peripheral involvement in content generation via Web 2.0 social technologies. These findings resonate with previous studies about the existence of a small cluster of digital residents in South African university contexts (Czerniewicz, 2009; Rambe and Bere, 2012). Students’ limited access to the Internet beyond institutional networks meant that even if they had Web‐ enabled devices, they needed substantial mobile data to remain connect to various Web‐based platforms like blogs, wikis, and Google documents. The digital divide thus played out through students' limited access to off‐ campus Internet. Similarly, Ng’ambi and Campbell (2012) highlight differential technology ownership and networked access judging from the few South African university students who owned laptops and had access to the Internet at home. However, the limited student application of educational software for content generation contradicts earlier research about the optimal use of emerging educational technologies in academia by educators (and indirectly by students) (Ng’ambi, et al, 2012). Ng’ambi et al.’s (2012) research on selected South African educators’ use of emerging technologies reports that the most used technologies in South African higher education institutions were research databases, social media, social networking, e‐books, web‐based documents and open‐education resources (OERs).

7. Implications for pedagogy The implications of these findings for pedagogy include:

Educators need to leverage student familiarisation and appropriation of a new generation of technologies to ensure that students capitalise on the educational value and affordances of these known but least exploited technologies.

408

Patient Rambe and Liezel Nel

Educators need to explore innovative academic uses of social media and digital recording / playback devices conceived largely as edutainment software (e.g. video and audio players). Since these technologies are conceived more as ancillary playful gadgets than mainstream educational technologies, educators need to generate appropriate contexts and sound learning activities that these technologies mediate to improve their relevance in mainstream education.

Educators should lobby their institutions to engage and partner with local MNOs to relax contractual obligations for students when purchasing high‐end mobile devices to ensure wider access to these technologies. MNOs, service providers and application developers need to be adopted and accommodated as critical stakeholders in technology‐mediated curriculum design to improve their responsiveness to network connectivity, mobile data provision and availability, student connection and familiarisation with academic applications.

8. Conclusion The current study examined CSI students’ preferences and extent of ownership of mobile devices and social technologies. The overall landscape of mobile technology adoption was suboptimal as ownership was concentrated in first generation mobile devices (low‐end mobile phones and laptops). Although student preferences for second‐generation mobile devices (tablet computers, e‐readers and netbooks) and other digital recording devices (MP3 players and video players) were impressive, their ownership of these handhelds was disappointingly low. Student concentration on using traditional technologies and their limited engagement in content creation positioned them within the instrumentalist approach, where technology use followed a replicative mode with limited opportunities for transformative learning. Mobile adoption was largely driven by contextual factors like the cost of technology acquisition, MNOs’ promotional opportunities for free communication and availability of networked connectivity. Personal factors like student subtle competition for new gadgets, perceptions about devices’ educational value and aesthetic value of technologies were additional considerations. With regard to student generation of mobile content, “Facebooking” and Tweeting were the main forms of content creation. Students only appropriated for their learning available technologies which they had familiarised themselves with and there was little evidence to support educators’ proactive role in student productive use of mobile devices. The digital profile of CSI students did not correspond to the widely popularised “digital resident” identity reported in literature. To the contrary, a “digital visitor” profile best suited their limited use of digital recording devices and concentration on a limited set of first generation mobile technologies.

References Centre for Educational Technology. (2010) Projects. [online] http://www.cet.uct. ac.za/projects Czerniewicz, L. and Brown, C. (2013) “The habitus of digital ‘strangers’ in higher education”. British Journal of Educational Technology, Vol 44 No 1 2013 44–53. Czerniewicz, L. (2009) “Cell phones and Sakai ‐ increasing access for all?” Paper read at Sakai 2009, Boston, Massachusetts, July. Cohen, L., Manion, L. and Morrison, K. (2007) Research methods in education. Sixth Edition, London: Routledge. Delport, C and Roestenburg, W. 2011 “Quantitative data collection methods: questionnaires, checklists, structured observation and structured interview schedules”. In A. De Vos, H. Strydom, C. Fouche’, C. Delport, (Ed.) Research at grassroots: For the social sciences and human service professions (pp. 171‐205). Pretoria: Van Schaik Publishers. Goldstuck, A. (2012) “Internet Access in South Africa to hit 20% penetration by 2013”. [online] Intergate Immigration (2013) “Online Media Stats for South Africa”. Posted 16. October 16, 2012 [online] http://www.intergate‐ immigration.com/blog/online‐media‐stats‐for‐south‐africa/(Accessed 06/05/2013. Keller, J. (1987a) “Development and Use of the ARCS Model of Instructional Design”. Journal of Instructional Development, Vol 10 No 3, 2‐10. Keller, J. (1987b) “Strategies for stimulating the motivation to learn”. Performance and Instruction, Vol 26 No 8, 1‐7. Keller, J. (1987c) “The systemic processes of motivation design”. Performance and Instruction, Vol 26 No 9‐10, 1‐8. Keller, J. M. (1984) “The use of the ARCS model of motivation in teacher training”. In K. Shaw and A. J. Trott (Eds.), Aspects of Educational Technology Volume XVII: staff Development and Career Updating (pp. 140‐145). London: Kogan Page. Litchfield, A., Dyson, L., Lawrence, E., and Zmijewska, A. (2007) “Directions for m‐learning research to enhance active learning”. Proceedings ascilite Singapore 2007, 587‐596. Ng’ambi, D, Gachago, D., Ivala, E. Bozalek, V. and Watters, K. (2012) “Emerging Technologies in South African Higher Education Institutions: Towards a Teaching and Learning Practice Framework”, In P. Lam (Ed.). Proceedings of the 7th International Conference on eLearning (pp. 354‐362), Hong Kong, The Chinese University of Hong Kong.

409

Patient Rambe and Liezel Nel Ng’ambi, D and Campbell, A. (2012) “Influence of Mobile Learning Discourse on Human Agency: A Critical Discourse Analysis Perspective”, In P. Lam (Ed.). Proceedings of the 7th International Conference on eLearning (pp. 346‐353), Hong Kong, The Chinese University of Hong Kong. Ng’ambi, D. (2011) “Enhancing Student Interaction in Didactics Teaching Approaches – The Right to Text During Class”. In Balcean, P. (Ed). The Proceedings of the 6th International Conference on e‐Learning (pp‐249‐257), University of British Columbia Okanagan, British Columbia, Canada, 27‐28 June 2011 Ng’ambi, D. and Rambe, P. (2008) “Barriers to students’ use of electronic resources during lectures”, South African Computer Journal, Vol 42, 47‐53. Nicholson, D. (2011) “Mobile Technologies ‐ Information on the Move … or Stuck in a Groove? ‐ A South African Perspective”. Library and Information Science Research Electronic Journal Vol 21, Iss 2, 1‐22. Peyper, L, (2013). Mobile phone usage in SA. Finweek. January 22, 2013 [online] http://finweek.com/2013/01/22/mobile‐ phone‐usage‐in‐sa/ Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9, 6, 1–9. Prensky, M. 2009. “H. sapiens digital: From digital immigrants and digital natives to digital wisdom”. Innovate Vol 5, No 3 [online] http://www.wisdompage.com/Prensky01.html Rambe, P and Bere, A. (In Press) “Using mobile instant messaging to leverage learner participation and transform pedagogy at a South African University of Technology” British Journal of Educational Technology. Rambe, P. and Bere, A. (2012) “An M‐Learning Strategy for leveraging learner participation: Using WhatsApp Mobile messaging at a South African University of Technology”, Paper presented at SACLA Conference, Black Mountain Leisure and Conference Hotel, Thaba ‘Nchu, 1‐3 July. Rao, M. (2011) Mobile Africa Report 2011: Regional Hubs of Excellence and Innovation, MobileMonday, March 2011. Rawlinson, C. (2011) “Infographic: Cell phone usage + South African mobile stats” [online] http://www.chrisrawlinson.com/2011/02/infographic‐cellphone‐usage‐sa‐mobile‐stats/ Voices of South Africa (2013). Telecommunications. [online] http://voicesofsouthafrica.com/category/telecoms. Wanjiku, R. (2011) “Africa banking on mobile to be first post‐PC continent” [online] www.computerworlduganda.com/articles/2011/04/05/africa‐banking‐mobile‐be‐first‐post‐pc‐continent. White, D. and Le Cornu, A. (2011). “Visitors and residents: A new typology for online engagement. First Monday” Vol 16, No. 9 [online] http://journals.uic.edu/ojs/index.php/fm/article/view/3171/3049 World Wide Worx (2012) “Internet Access in South Africa 2012: Broadband in SA doubles in two years” Executive Summary, Johannesburg, 1‐4, [online] www.worldwideworx.com.

410

Student Perceptions on the Usefulness of Educational Technologies at a South African University Patient Rambe and Liezel Nel Department of Computer Science and Informatics, University of the Free State, South Africa pjoerambe@gmail.com liezel@ufs.ac.za Abstract: As students are becoming exposed to ubiquitous technologies in higher education, critical questions are being posed about the extent of appropriate use and meaningful deployment of these technologies for their learning. Given that technological access and ubiquity do not necessarily guarantee effective use of technologies ‐ especially in resource‐ stricken South African learning environments ‐ these questions befit further interrogation in academic circles. Productive use of educational technologies tends to be tied to student perceptions about the relevance of technologies in their courses including their confidence in effective deployment of educational technologies. The Bourdieusian concepts of field, habitus and forms of capital as well as a Departmental Information and Communication Technology (ICT) climate survey were drawn upon to explore Computer Science and Informatics students’ perceptions of specific educational technologies and barriers to their effective use in university learning contexts. Findings suggest that students rated learning management systems, lecture slides, video tutorials, e‐mails and digital textbooks as the most invaluable technologies for their learning compared to other instructional technologies. They also ranked limited wireless access on campus, slow Internet connectivity, and educators’ sub‐optimal use of educational technologies as the main impediments to their effective appropriation of educational technologies. Implications for pedagogy include the need for educators’ to extend technology use from traditional technologies to emerging Web‐based technologies, explore the rationale for student underutilisation of new technologies and find new leverage points for optimising their usage. Keywords: educational technologies, ubiquitous technologies, student perceptions, resource poor environments, higher education

1. Introduction The hype about universal institutional access to educational technologies by young adults who “grew up with technology” has reinforced a technophilic discourse about “digital natives” (Prensky, 2001) who have mastered the craft of productively using educational technologies. This euphoric discourse on universal access lack substantial evidence especially in the often resource constrained educational environments of South Africa. On the contrary, there is sufficient evidence that asymmetrical student access to educational technologies – notwithstanding students’ euphoric orientation towards educational technologies – has limited student productive appropriation of traditional and emerging technologies. For example, South African literature concurs on the skewed variations in student access to and differential use of educational technologies on and off campus (Czerniewicz and Brown, 2009; Brown and Czerniewicz, 2010; Ng’ambi and Campbell, 2012; Czerniewicz and Brown, 2013). While university students’ institutional access to educational technologies like computers and learning platforms is equal and balanced, off‐campus access is unequal and uneven (Czerniewicz and Brown, 2009). More so, in South Africa, sharp regional binaries and variations in household access to computers and the Internet persist, with a far higher concentration of such technologies in the advanced regions like the Western Cape and Gauteng provinces (Tlabela et al, 2007). We are of the opinion that any technocratic discourse on technology access and use that downplays actual student experiences and fails to recognise the multiple barriers to students’ appropriation of educational technologies is shortsighted and counterproductive. Mindful of the emerging South African research on “digital strangers” – those who lack exposure and productive mediated experiences with technology (Czerniewicz and Brown, 2013) – student access, perceptions towards and impediments to meaningful appropriation of educational technologies are worthy of investigation. This is critical to engender inclusive learning environments that eradicate student academic alienation, heighten their retention rates and engender meaningful learning experiences. This paper therefore explores a group of Computer Science and Informatics (CSI) students at a South African university’s perceptions of specific educational technologies in order to answer the following questions:

411

Patient Rambe and Liezel Nel

What are CSI students’ perceptions of the traditional and emerging educational technologies availed in their university courses?

Which social and cultural‐historical influences shape these perceptions towards educational technology?

Which contextual, technological, and pedagogical barriers impede students’ effective use of these educational technologies in their courses?

In this paper, traditional technologies imply those web‐based learning platforms, application and tools that higher education practitioners are familiar, confident and comfortable with due to their long tradition of pedagogical application. Emerging technologies are regarded as those that are “likely to have a large impact on teaching, learning, or creative expression within higher education” within the next one to five years (Johnson et al, 2011). We infer that the educational impact of such emerging technologies is still under exploration while their relevance is still uncertain or unknown.

2. Literature review Multiple studies have been conducted on student perceptions of educational technology. These studies have compared student preferences for proprietary learning management systems (LMSs) with open source LMSs (Carvalho, Areal and Silva, 2011), explored student perceptions of e‐assessments (Dermo, 2009) and investigated student perceptions of the educational value of blogs (Goh, Quek and Lee, 2010). Similar studies have interrogated reflective technologies like vlogs (video blogs) (Hung, 2011) and electronic speaking portfolios (Huang and Hung, 2010) to unravel how these technologies provide English‐as‐a‐foreign‐language students with opportunities to appropriate the target language and improve performance in speaking the foreign language respectively. Although the aforementioned studies illuminate understanding of student differential adoption of learning platforms, explain the slightly positive attitudes towards e‐assessments and describe the impact of vlogs on student self‐organisation and refection, they do not provide insights into the influence that students’ socio‐cultural and historical backgrounds have on their successful adoption of educational technologies. As such, little is known about the relationship between student cultural profiles and their productive use of these technologies. A handful of studies, however, consider the influence of socio‐cultural and contextual influences on student perceptions towards educational technology (Selwyn, 2007; Lui, 2010; Hung, 2011). Liu (2010) examined adolescents’ perceptions of educational robots and the learning of robotics to develop a taxonomy for describing student perceptions towards educational robots. We interpret that social context of technology use potentially influenced student perceptions of future career paths and meaningful deployment of technologies. Other studies are rooted in the relationship between the immediate context of technology use, student prior knowledge and student preferences for particular technologies (Kolikant, 2009; Saeed, Yang and Sinnappan, 2009; Lee and Chen, 2010). Kolikant (2009) explored the impact of computer and Internet technologies on the learning preferences of students whose schools did not use these technologies in class. The study reports on how the informal experiences of some classes with technology stirred the belief that students were more knowledgeable than their educators leading to their devaluating of educators’ instructional practices. Lee and Chen (2010) explored 580 Taiwanese high school learners’ adoption of a virtual manipulatives program in Mathematical problem solving. Students who were less fearful of mathematics and who conceived mathematics as an invaluable course tended to consider the academic application of manipulatives as a beneficial strategy of problem solving. Our inference is that students’ background knowledge of Mathematics influenced their perspectives on the academic value of virtual manipulatives. Similarly, Saeed et al (2009) developed a research model that bridged emerging technologies (deployed in higher education), student learning styles and technology preferences. In a case study, conducted to validate their model, they unravelled the relationship between students’ learning styles and their preferences for instructional strategies. Their findings highlight the flexibility of student learning styles, the accommodation of varying technology‐dependent instructional strategies and the fact that the technology preferences of students were not limited to a particular tool. For “digital natives”, therefore, the technology‐saturated environments which they had grown up in potentially shaped their flexible adoption and perceptions of multiple technologies (Czerniewicz and Brown, 2013).

412

Patient Rambe and Liezel Nel

3. Theoretical framework The theoretical framework used in this paper is derived from the Bourdieusian concepts: field, habitus and forms of capital. The three forms of capital are economic, social and cultural capital – with only the last two relevant to this study.

3.1 Field The Bourdeiusian conception of “field” describes the wider world, which is in a dialectical relationship with an individual’s subjective experience (often simplified as the habitus) (Gauntlett, 2011). For Goldthorpe (2007) fields constitute the different social domains over which dominant classes are able to extend and reinforce their power and privileges. In academia, a field constitutes a disciplinary sphere – including its broader social and technology‐mediated context – upon which more accomplished students and academic experts exercise their academic authority over peers and novices respectively.

3.2 Habitus A habitus is a “system of lasting, transposable dispositions which, integrating past experiences, functions at every moment as a matrix of perceptions, appreciations, and actions” (Bourdieu, 1971:83 cited in Mendoza, Kuntz and Berger, 2012:560). Mendoza et al (2012) highlight that a habitus reinforces a taken‐for‐granted common representation of the world in a class‐specific manner at a cognitive level, which allows certain preferences and tendencies to become routinized as part of an individual’s worldview. A student’s habitus constitutes a discursively generated psychological orientation, shaped by their technological background and social upbringing, which predisposes them to perceive, act and behave in particular ways when confronted with particular technological or academic challenges. It describes a socially constituted cognitive capacity, a long standing disposition of the mind that shapes and influences the individual exploitation of opportunities presented to him by the objective field (Bourdieu, 1986). A habitus constitutes the specialisation of consciousness peculiar to a particular field (Gray and Whitty, 2010). Essentially, it is an unconscious, internalised force that predisposes individuals to their given social classes, which awards them a certain type and/or amount of cultural capital they can use to take advantage of opportunities available to them in a given context or field (Bourdieu, 1977, cited in Wells and Lynch, 2012). Impliedly, a student who had limited prior knowledge and access to particular educational technologies in his upbringing may be predisposed differently compared with his/her peers with regard to identifying and capitalising on the educational affordances of technology. Therefore, a habitus constitutes a disposition to act in particular ways when confronted with particular situations, itself a consequence of human actions’ regulation by a set of durable and generative principles (Wells and Lynch, 2012). For Bourdieu (1977, cited in Huddleston, 2012) a habitus describes the subconscious tendencies people have to think and behave in specific ways. For him, its represents the transfer of the objective rules of the field into the subjective thoughts and actions of the agents.

3.3 Forms of capital Bourdieu (1986:47) distinguishes among three forms of capital:

Economic capital, which is immediately and directly convertible into money and may be institutionalised in the form of property rights;

Cultural capital, which is convertible, into economic capital and may be institutionalised in the form of educational qualifications. For Goldthorpe (2007) this is capital “embodied” in individual dispositions and competencies that give privileged access to its “objectified” form of cultural artefacts, evaluations and educational qualifications.

Social capital, comprising social obligations (“connections”), which are convertible, in certain conditions, into economic capital and may be institutionalised in the form of a title of nobility. Social capital is the sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalised relationships of mutual acquaintance and recognition (Bourdieu and Wacquant, 1992:119). For example, middle class intellectuals who are well connected tend to secure the most prestigious jobs and opportunities in ways that exclude outer groups from enjoying similar opportunities.

413

Patient Rambe and Liezel Nel

4. Methodology The survey approach was adopted in this research. Cohen, Manion and Morrison (2007) highlight that surveys gather data at a particular time with the intention of describing the nature of conditions or determining the relationships that exist between different events or variables. In our study, the intention was to explore the nature of CSI students’ perceptions (through frequency counts) of educational technologies availed at their university including the social and cultural‐historical influences that shape these perceptions towards educational technology, and the barriers that impede their effective use of these technologies. A questionnaire with Lickert scale‐based questions was designed to capture student perceptions about the usefulness of specific technologies and barriers to technology adoption in their courses. The questionnaire was uploaded onto a Website and students were invited to log on to participate in the survey. The principles governing research into the study on human subjects of informed disclosure, voluntary participation, right to withdraw from the research without any risk and anonymity of participants were observed. A total of 109 students participated in the survey. The group comprised of 60 first year, 35 second year and 14 third year students. However, only 92 students responded to the question on perceptions towards educational technology while 87 responded to the question on barriers to the use of educational technologies respectively. The results of the survey were entered into Microsoft Excel and frequency tables were generated to determine the frequency of responses.

5. Findings and discussion 5.1 Student perceptions of useful technologies Overall, the students regarded traditional technologies as much more useful than emerging technologies (see Table 1). Table 1: Student perceptions of the usefulness of educational technologies provided in their courses Educational Technology Learning management system Lecturer's PowerPoint slides Video tutorials E‐mail (to or from lecturer) Digital textbooks Texting or instant messaging Visualisation tools or simulations Online self‐tests Online discussion tools Collaborative documents Chat tools Blogging tools Student response systems

Never Used (1) 1.1% 1.1% 7.6% 8.7% 17.4% 5.4% 21.7% 23.9% 16.3% 29.3% 35.9% 55.4% 52.2%

Not useful at all (2) 2.2% 1.1% 4.3% 0.0% 7.6% 8.7% 8.7% 5.4% 18.5% 15.2% 16.3% 15.2% 20.7%

Slightly useful (3) 1.1% 7.6% 15.2% 22.8% 19.6% 32.6% 19.6% 32.6% 29.3% 20.7% 23.9% 19.6% 20.7%

Moderately useful (4) 19.6% 17.4% 17.4% 27.2% 19.6% 23.9% 29.3% 18.5% 23.9% 23.9% 14.1% 6.5% 4.3%

Very useful (5) 76.1% 72.8% 55.4% 41.3% 35.9% 29.3% 20.7% 19.6% 12.0% 10.9% 9.8% 3.3% 2.2%

Rating Average 4.67 4.60 4.09 3.92 3.49 3.63 3.18 3.04 2.97 2.72 2.46 1.87 1.84

CSI students perceived the Blackboard LMS (76.1%); lecturers’ PowerPoint slides (72.8%), video tutorials (55.4%), e‐mail communication with lecturers (41.3%) and digital textbooks (35.9%) as the most useful technologies in their studies. It seems that the strong connection these technologies have with instructional delivery explains their considerable popularity compared to social technologies like chat rooms, blogs and online response systems. However, some educational technologies were considered not as useful as the aforementioned technologies, notwithstanding their pedagogical value. These include: visualisation tools (including simulations and animations) (20.7%), online self‐tests (19.6%) and web‐based threaded discussions (12.0%). On the extreme, the majority of the students claimed that they had never used student response systems (clickers) (52.2%) and blogs (55.4%). The absence of student response systems from the classrooms can in part be attributed to the considerable financial resources needed to acquire the necessary response devices.

414

Patient Rambe and Liezel Nel

5.2 Social and cultural‐historical influences on student perceptions towards educational technology The data presented in Table 1 can be explained in two ways: 1) The influence of social context on student technology use, and 2) student prior exposure and knowledge of particular educational technologies, which shaped their perceptions towards educational technologies. Firstly, in Bourdieusian terms, the students’ field of academic practice shaped their rankings of the most desirable technologies – those which directly contributed to their academic performance and which educators had direct control over. The field often imposes implicit rules of engagement which agents subconsciously accept as structures ingrained in their psychology and which shape their decisions and actions (Huddleston, 2012). The largely replicative use of traditional technologies by lecturers in the CSI department resonated with their students’ high preferences for educator‐centred technologies, which suggests that students are being enframed by their field of educational practice. Secondly, the students’ prior ICT knowledge and exposure ‐ including individual capabilities and competencies (cultural capital) – shaped their perceptions of educational ICTs. As Bourdieu (1986), highlights, cultural capital helps explain the unequal scholastic achievement of learners originating from the different social classes by relating academic success to the distribution of cultural capital between the classes. As such, differences in cultural capital could explain the sharp variations in student perceptions of the usefulness of educational technologies. More so, students’ varied interpretations of the academic value of technologies – involving a majority affirmation of educator‐centred, transmission based technologies and a minority acknowledgement of a wider suite of technologies – could be interpreted as a consequence of sharp variations in their technological habitus. Since habitus describes a subconscious tendency of agents to act and behave in specific ways (Bourdieu, 1977, cited in Wells and Lynch, 2012), its logical that only a few students possessed the psychological dispositions for discerning and acting on the prospective educational opportunities derived from familiarisation with multiple technologies (i.e. technological habitus). This finding on fewer students with technological habitus is backed by Czerniewicz and Brown (2013) who argue that at South African universities, only the digital elite meets the criteria of a “digital native”, that is, a technology user who has grown up with digital technology, who comes to university familiar with computers, and is purported to learn to use computers informally. As such, only few students with more cultural capital discerned the learning opportunities in appropriating diverse technologies (see the distribution of responses in Table 1). We also inferred that students who held positive perceptions about multiple technologies might also be those who made effective use of their friendship networks to broaden their knowledge base (social capital). As literature suggests, students in similar contexts and with related expectations constitute advantageous social capital because peers help them develop a habitus (Wells and Lynch, 2012) on their technological choices.

5.3 Barriers to student use of educational technology in their modules The CSI students identified various barriers that constrain their use of educational technology in their courses (see Table 2). Table 2: Barriers to CSI students’ use of educational technology in their courses Potential Barriers

Never used (1)

Large barrier (2)

Moderate barrier (3)

Small barrier (4)

Not a barrier (5)

Average rating

Finding wireless access on campus

9.5%

33.3%

20.7%

16.1%

10.3%

2.64

Slowness of wireless internet connection

16.1%

32.2%

21.8%

14.9%

2.80

Cost of software

3.4%

31.0%

11.5%

18.4%

35.6%

3.52

9.5%

31.0%

18.4%

16.1%

14.9%

2.76

4.6%

27.6%

23.0%

25.3%

19.5%

3.28

Cost of printing

5.7%

25.3%

13.8%

29.9%

25.3%

3.44

Lecturers not using educational technologies well

3.4%

21.8%

26.4%

32.2%

16.1%

3.36

Successfully connecting to wireless access points on campus Lecturers not using educational technologies at all

415

Patient Rambe and Liezel Nel

Potential Barriers

Never used (1)

Large barrier (2)

Moderate barrier (3)

Small barrier (4)

Not a barrier (5)

Average rating

Slowness of hard‐wired internet connection

10.3%

21.8%

20.7%

13.8%

33.3%

3.38

Lack of technical support

6.9%

20.7%

16.1%

28.7%

27.6%

3.49

Printing problems

4.6%

20.7%

13.8%

27.6%

33.3%

3.64

Problems with my computer

2.3%

19.5%

13.8%

25.3%

39.1%

3.79

4.6%

13.8%

11.5%

32.2%

37.9%

3.85

6.9%

12.6%

8.0%

31.0%

41.4%

3.87

Problems using Blackboard site

3.4%

9.2%

24.1%

32.2%

31.0%

3.78

Problems using Google sites

5.7%

9.2%

13.8%

25.3%

46.0%

3.97

Amount of time needed to use educational technologies Amount of time needed to learn educational technologies

Students perceived finding wireless access on campus (33.3%), slowness of wireless internet connection (32.2%), cost of software (31.0%), successfully connecting to wireless access points on campus (31.0%), and lecturers who are not using educational technologies at all (27.6%) as the main impediments to their productive use of educational technologies. The main barriers, therefore, were related to the multiple challenges – connectivity, pedagogical application of technologies and software problems. Student interpretations of network problems as the most acute technology challenges might be a consequence of their internalised subjective beliefs about their objective context of academic engagement (their field). As Wells and Lynch (2012) suggest, students may form a disposition to make particular choices or decisions about academia matters based on internalised beliefs about the institution developed from their immediate environment (that is, field). Since searching on online databases, downloading and use of different software and developing applications constitute some of the critical learning activities in the CSI field, it is logical that connectivity and software issues ranked among the highest barriers. The second band on the hierarchy of barriers related to cost of printing (25.3%), lecturers who are not using educational technologies well (21.8%), slowness of hard‐wired internet connection (21.8%), lack of technical support (20.7%) and printing problems (20.7%). The variations in the ranking of different barriers suggest different student experiences of the challenges of the field based on their economic and cultural capital. The categorisation of printing costs as a moderate barrier by a minority suggests student variations in income (economic capital) while the lack of technical support alludes to differences in cultural capital (student capabilities, competences and skills) to deal with technology related problems. These findings buttress Bourdieu and Passeron’s (1990, cited in Gray and Whitty, 2010) view that no two individuals possess identical histories – the same way as there are no two identical individual habituses. On the contrary, those students who concurred in their ranking of multiple variables could be conceived as located in the same social field by virtue of having similar dispositions and sharing similar conditions. Drawing on Bourdieu’s work, Mendoza, et al (2012) acknowledge that agents located in proximity within social fields are likely to exhibit common dispositions that translate into similar practices and representations.

6. Discussion Firstly, student perceptions about the usefulness of technologies depended on the perceived academic relevance of the technology and especially its direct contribution to their learning of course material. This buttresses earlier studies which reported that students had positive attitudes towards technologies that increase content‐area learning of courses (Drennan, Pisarski and Kennedy, 2005; Boon, Fore and Rasheed, 2007). Drennan et al (2005) highlight that student satisfaction with their courses depended on their positive perceptions of technology including the support technologies (computers, course websites, and removable disks) provided for autonomous learning modes. These studies implicitly reinforce the view that the relevance of technologies to a particular course positively influences student perceptions towards educational technology. econdly, the social context of technology use (the social field), student prior exposure and knowledge of particular educational technologies shaped their perceptions of educational technologies. It seems that technologies which are introduced to students by their educators early in their university studies

416

Patient Rambe and Liezel Nel became more mainstay technologies than those which students explored for themselves. This finding support prior research about the impact of educators’ use of educational technologies such as PowerPoint slides on student academically productive behaviours (like attendance of classes and participation in discussions) as the technology helped them remember taught concepts (Parker, Bianchi and Cheah, 2008). Although the study of Parker et al (2008) does not report on the impact of educators’ technology‐enabled instruction on student technological behaviour, it has some resonance with the social influence of a knowledgeable other on student academic conduct. The finding on student positive perceptions of educator‐centred technologies partially concur with the findings of Ng’ambi et al (2012) that the use of educational technologies at 22 selected South African higher educational institutions ranged from prescriptive/ replicative (pedagogical practices based on pre‐determined knowledge) towards emergent/ transformative (student‐driven pedagogy where learning arises out of student interactions with resources and other students). The use of one‐way transmission technologies (e.g. LMSs, PowerPoint slides and online video tutorials) supports prescriptive pedagogy but not transformative learning as it inadequately supports student‐peer and student‐community engagements. Ng’ambi and Campbell (2012) document how South African first year Mathematics students used mobile SMSing to interact and engage with learning resources. In this study, the prescriptive component of delivery dominated because the educator designed the learning tasks, appropriated technological tools such as the LMS and mobile devices, and invited students to engage with learning resources. Students also judged the usefulness of technologies on the basis of their extent of relevance to their courses and educational outcomes. For instance, educator‐centric technologies that fostered student‐educator communication and knowledge sharing (e‐mails), direct provision of educational content through knowledge repositories (LMSs, PowerPoint slides) and illustrative technologies for clarifying complex concepts (online video tutorials) were more popular than other Web‐based technologies that student explored themselves. The purpose for which technologies are deployed determines their relevance. Afolabi and Abidoye (2011) document the academic essence of technologies: the communicative capacity of e‐mails and their its potential to breach barriers to information transfer, the capacity of the Internet to connect various user networks and the potential of digital libraries to store volumes of data unconstrained by physical space. In terms of the hierarchy of barriers, the first band seemed to be tied to a sub‐optimal technological infrastructure where erratic connectivity, weak connection signals and slow internet activities were the norm rather than an exception. The moderate band emphasised printing services, pedagogical and technical issues. Student claims about educators’ non‐use of educational technologies in their courses might be attributed to educators’ frustrations with these aforementioned impediments. For instance, weak connectivity often militates against educators’ and student’s executions of bandwidth‐intensive learning activities like online video tutorials, virtual simulations and animations. Ng’ambi et al (2012) report that bandwidth‐intensive technologies (e.g. game based learning, augmented reality and virtual worlds) have not been sufficiently exploited at South African higher education institutions. For example, of the 262 educators they investigated, 82 per cent of them had never used games and massively multiplayer online games (MMOGs) and 27 per cent had never heard about augmented reality.

7. Implications for pedagogy The findings have the following implications for pedagogy:

Educators should broaden their adoption of new technologies like social media as their appropriation by students predominantly depends on educators’ familiarisation and participation in them. This would shift educational delivery from the current replicative towards the transformative mode that supports student engagement with content and meaningful interaction.

Educators’ e‐learning innovations should embrace ubiquitous technologies students are familiar with, conversant with and those which they bring to the classroom environment. Most importantly, educators need to draw on the competencies and capacities that students have established (like social networking, texting) and those which are emerging (such as creation of new artefacts) as well as devise way of accommodating and integrating them into the technology‐mediated academic tasks they give to students.

An important point of departure for technology‐based innovations is to strategically select a range of productive ubiquitous technologies that students have prior exposure, passionate about comfortable adopting for learning. A preconceived pedagogical strategy is then adjusted and aligned to the functional

417

Patient Rambe and Liezel Nel affordances and various educational potential of these technological tools to create a flexible experimental pedagogy lab for advancing new competencies, skills and literacies.

Since students valued highly those technologies that they conceived to directly impact their educational outcomes, curriculum design should continually be reviewed and aligned with pedagogy and new technological provisions to create highly effective technology‐enhanced learning environments for students.

The sub‐optimal use of conversational technologies (discussion forums and blogs) despite their support for constructivist learning implies that educators needs to demonstrate the academic relevance of these technologies by providing ideal instructional models that draw on them.

Varying student prior exposure and experience with ICTs seemed to explain the subtle differences in their perceptions towards educational technologies. Educators should develop instructional strategies with learning activities designed to support or require student use of ubiquitous technologies like mobile devices and applications.

Although the barriers to connectivity and slow internet might be beyond educators’ purview, educators are ideally positioned as technology champions to lobby institutional leaders and ICT services to upgrade the processing capacity of lab computers and develop interventions for integrating institutional networks, platforms and complementary student‐owned technologies.

Since students conceived erratic connectivity, weak connection signals and slow internet connections as impediments to their use of certain technologies and web‐based learning, a broader, shared pedagogical strategy that involves and sensitizes senior management and ICT services departments to these critical issues could be the first bold step towards new technological acquisitions that are academically rewarding.

A shared understanding of the aforementioned technology‐related constraints among educators, senior management and ICT services departments could trigger interventions aimed at upgrading the intranet, increasing bandwidth for student, identifying and closing the leakages within the technological infrastructure system.

8. Conclusion This paper examined CSI students’ perceptions of educational technologies, social and cultural‐historical influences shaping these perceptions and barriers to student effective use of educational technologies in their courses. Findings suggest that students considered educator‐centric technologies as the most useful technologies in their learning. Although they expressed varied opinions about the educational usefulness of other Web‐based social technologies (discussion forums, chat rooms, collaborative documents and blogs), their use was peripheral compared to transmission‐based, educator‐driven technologies. A combination of social, cultural‐historical factors influenced student perceptions of the educational usefulness of technologies. In the social context, educators’ promotion and use of particular technologies, the capacity of technology to enhance successful learning (technological relevance), and the cultural capital and technological habitus of students influenced their perspectives on educational technologies. Prior ICT knowledge, competencies and skills (cultural capital) tended to enhance student capacity to identify and perceive more educational opportunities for adopting multiple technologies for learning. As such, students’ differential technological habit uses invariably shaped their productive use of educational technologies. Lastly, student barriers to educational technologies were largely network, software and pedagogically related – issues that necessitate the concerted efforts from educators, ICT services and university administration to ensure the upgrading of computer networks and seamless integration of institutional and student‐owned technologies. Since students tended to adopt the technologies used by educators, these educators can play a more effective “technology champion” role by trialling new technologies (social media, new documentation software e.g. Google drive) to establish their new affordances and niche areas, showcase good examples of their productive educational uses to students and the broader academic community, and reward best technology‐mediated learning practices exhibited by students.

References Afolabi, A. and Abidoye, J. (2011) “The integration of information and communication technology in library operations towards effective library services”, Proceedings of the 1st International Technology, Education and Environment Conference, African Society for Scientific Research (ASSR) and Human Resource Management Academic Research Society (pp 620‐628).

418

Patient Rambe and Liezel Nel Boon, R., Fore, C., and Rasheed, S. (2007) “Students' Attitudes and Perceptions toward Technology‐Based Applications and Guided Notes Instruction in High School World History Classrooms”, Reading Improvement, Vol. 44, No. 1, pp 23‐31. Bourdieu, P., and Wacquant, L. (1992) An Invitation to Reflexive Sociology. Chicago: University of Chicago Press. Bourdieu, P. (1986) “The forms of capital”, In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education, New York, Greenwood. Brown, C. and Czerniewicz, L. (2010) “Debunking the digital native: beyond digital apartheid, towards digital democracy”, Journal of Computer Assisted Learning, Vol. 26, pp 357‐369. Carvalho, A., Areal, N. and Silva, J. (2011) “Students’ perceptions of Blackboard and Moodle in a Portuguese university”, British Journal of Educational Technology, Vol. 42, No. 5, pp 824‐841. Cohen, L., Manion, L. and Morrison, K. (2007) Research methods in education, Sixth Edition, London, Routledge. Czerniewicz, L. and Brown, C. (2009) “A virtual wheel of fortune? Enablers and constraints of ICTs in higher education in South Africa”, In W. Marshall (Ed.), Bridging the knowledge divide: educational technology for development (p. 57– 76), Colorado, Information Age Publishing. Czerniewicz, L. and Brown, C. (2013) “The habitus of digital ‘strangers’ in higher education”, British Journal of Educational Technology, Vol. 44, No. 1, pp 44‐53. Dermo, J. (2009) “e‐Assessment and the student learning experience: A survey of student perceptions of e‐assessment”, British Journal of Educational Technology, Vol. 40, No 2, pp 203‐214. Drennan, J., Pisarski, A. and Kennedy, J. (2005) “Factors affecting student attitudes toward flexible online learning in Management Education”, The Journal of Educational Research, Vol. 98, No. 6, pp 331‐338. Gauntlett, D. (2011) “Three approaches to social capital”. In D. Gauntlett, The social meaning of creativity, from DIY and knitting to YouTube and Web 2.0, Polity Press. Goh, J., Quek, C.J. and Lee, O.K. (2010) “An Investigation of Students' Perceptions of Learning Benefits of Weblogs in an East Asian Context: A Rasch Analysis”, Educational Technology & Society, Vol. 13. No. 2, pp 90‐101. Goldthorpe, J. (2007) “Cultural Capital: Some Critical Observations”, Sociologica, Società editrice il Mulino, Bologna, pp 1‐ 23. Gray, S. and Whitty, G. (2010) “Social trajectories or disrupted identities? Changing and competing models of teacher professionalism under New Labour”, Cambridge Journal of Education, Vol. 40, No. 1, pp 5‐23. Huang, H. and Hung, S. (2010) “Implementing electronic speaking portfolios: perceptions of EFL students”, British Journal of Educational Technology, Vol. 41, No. 5, pp E84‐E88. Huddleston, A. (2012) “Understanding responses to high school exit exams in literacy: A Bourdieusian analysis of poetic transcriptions”, Journal of Adolescent & Adult Literacy, Vol. 55, No. 8, pp 734‐744. Hung, S. (2011) Pedagogical applications of Vlogs: An investigation into ESP learners’ perceptions, British Journal of Educational Technology, Vol. 42, No. 5, pp 736‐746. Johnson, L., Smith, R., Willis, H., Levine, A. and Haywood, K. (2011) The 2011 Horizon Report, Austin, Texas, The New Media Consortium. Kolikant, Y. (2009) “Digital Students in a Book‐Oriented School: Students’ Perceptions of School and the Usability of Digital Technology in Schools”, Educational Technology & Society, Vol. 12, No. 2, pp 131‐143. Lee, C. and Chen, M. (2010) “Taiwanese junior high school students’ mathematics attitudes and perceptions towards virtual manipulatives”, British Journal of Educational Technology, Vol. 41, No. 2, pp E17–E21. Liu, E. (2010) “Early adolescents’ perceptions of educational robots and learning of robotics”, British Journal of Educational Technology, Vol. 41, No. 3, pp E44–E47. Mendoza, P., Kuntz, A. and Berger, J. (2012) “Bourdieu and Academic Capitalism: Faculty ‘Habitus’ in Materials Science and Engineering”, The Journal of Higher Education, Vol. 83, No. 4, pp 558‐581. Ng’ambi, D. and Campbell, A. (2012) “Influence of Mobile Learning Discourse on Human Agency: A Critical Discourse Analysis Perspective”, In P. Lam (Ed.), Proceedings of the 7th International Conference on eLearning (pp. 346‐353), Hong Kong, The Chinese University of Hong Kong. Ng’ambi, D., Gachago, D., Ivala, E., Bozalek, V. and Watters, K. (2012) “Emerging Technologies in South African Higher Education Institutions: Towards a Teaching and Learning Practice Framework”, In P. Lam (Ed.), Proceedings of the 7th International Conference on eLearning (pp. 354‐362), Hong Kong, The Chinese University of Hong Kong. Parker, R.E., Bianchi, A. and Cheah, T.Y. (2008) “Perceptions of Instructional Technology: Factors of Influence and Anticipated Consequences”, Educational Technology and Society, Vol. 11, No. 2, pp 274‐293. Prensky, M. (2001) “Digital natives, digital immigrants. On the Horizon, Vol. 9, No 6, pp 1‐9. Saeed, N., Yang, Y. and Sinnappan, S. (2009) “Emerging Web Technologies in Higher Education: A Case of Incorporating Blogs, Podcasts and Social Bookmarks in a Web Programming Course based on Students' Learning Styles and Technology Preferences”, Educational Technology and Society, Vol. 12, No. 4, pp 98‐109. Selwyn, N. (2007) “e‐Learning or she‐learning? Exploring students’ gendered perceptions of education technology”, British Journal of Educational Technology, Vol. 38, No 4, pp 744‐746. Tlabela, K., Roodt, J. Paterson, A., and Weir‐Smith, G. (2007) Mapping ICT access in South Africa, Pretoria, HSRC Press. Wells, R. and Lynch, C. (2012) “Delayed College Entry and the Socioeconomic Gap: Examining the Roles of Student Plans, Family Income, Parental Education, and Parental Occupation”, The Journal of Higher Education, Vol. 83, No. 5, pp 671‐693.

419

Digital Services Governance With AGIMUS David Reymond Université de Toulon, France david.reymond@univ‐tln.fr Abstract: Today 98% of students have access to a bouquet of digital services through their cyberspace. Pending of settlement and maintenance costs, the question arises to measure the usefulness of the panoply of services deployed. If this is straightforward locally for a bunch of services, the same question independently of the technology would offer more details on student’s behaviour. From the point of view of an entity providing digital services, at the level of governance or policy makers they may also need other indicators such as the number of services offered and their respective rates of use. We show how to avoid most technical issues, and we deal hereafter with determining the good levels of usage. The Agimus system, an open and free application is used to generate a bundle of indicators. Agimus is based on normative repositories of description of user profiles. We'll show how the device paves the way in a quality approach for improvement of the digital services provided by one or more organizations, the co‐construction of knowledge, among others, on the statistical behaviour of the client users. Examples of indicators implemented will be taken on institutions of higher education digital services. Keywords: cyberspace, usages, indicators, log analysis, consumer behaviour, policy making

1. Introduction Indicator systems have emerged in the context of increased awareness of the importance of analysing performance. On the one hand, the importance of data to inform rational decision‐making needs no further proof, and in the context of policies designed to give more autonomy to institutions, verifying the performance of these institutions has become an obligation. Indicators are the main matter for ranking institutions, and in the university context, these rankings nowadays play a significant role in university decision making (Hazelkorn, 2008). Interested reader could retrieve further information on the most known but discussed rankings: The Times Higher Education World University Rankings (Baty, 2011), the Scimago Institutions Ranking which includes also other research institution in addition to universities (cf. their web site: www.scimagoir.com), the Academic Ranking of World Universities (ARWU; www.arwu.org), commonly known as the Shanghai ranking (Liu and Cheng, 2005) and the Leiden Ranking based on bibliometrics indicators of publication output, citation impact, and scientific collaboration (Waltman et al., 2012). We deal hereafter with another kind of indicators, which address the usage degree of the cyberspaces developed by institutions. In France, for each of the policy objectives in the Loi organique pour la loi de finances (LOLF), the government of France set one or several indicators to monitor the progress and attainment of these objectives. In Table 4, (Martin and Sauvageot, 2011, 38) details the matching indicators with objectives in this country. Item 12, within the objective to “optimize the management of higher education institutions, including the management of facilities” specifies the indicator “Utilization rate of facilities”. In the French higher education, many digital services (email, intranet, forums, Web site publishing, diaries, school‐related services, documentation services, budget management and accounting, online courses, and others) are now available to students, teachers and administrative staff. They form part a facilities provided by the institutions. Historically, the Ministry of National Education, Higher Education and Research has decided to promote the deployment of digital services through several actions, one of these is the digital universities in the region (Université Numérique en Région ‐ UNR) that has to meet strategic objectives:

Offer of a bunch of digital services to the university community (students, teachers, administrative staff and researchers)

Take into account access to the infrastructure for all students (collective and individual equipment, networks, etc.)

Offer support for digital uses.

Today, the digital mission for higher education (Mission Numérique pour l’Enseignement Supérieur ‐ MINES), within the French Ministry of Higher Education, monitors and is the national coordination of UNR. Currently 17 UNR fully cover the national territory. Since there, we have identified through a survey (Reymond and Dib, 2008) a range of facilities deployed as a variety of digital services in a very heterogeneous way. Hence this is a

420

David Reymond characteristic of the cyberspaces at the national level. The maturity of these portals settlement is reached, however. The main digital services deployed within UNRs revolve mainly around registrar processes, e‐ education, collaborative tools and communication, library resources, university life and relation‐ships with companies. But o lot of other services can be deployed independently by institutions. Thus, according to the latest estimations, 98% of French students have access to a bunch of digital services. In order to provide a quality service to the end user, the MINES wishes to provide to the UNR a device for automatic build usage indicators of the digital services deployed. The immediate challenge using those indicators is to be able to evolve and adapt these digital services, the "virtual" campus, helped by the factual usage data that are to be made. If each digital service can produce its own data traces of activity, the data are strongly related to the application technology service, the data is costly and time‐consuming to analyse, and generally not interoperable: using simple data trace, it is very difficult, for instance to analyse the users' navigation between services). Furthermore, the user IDs coding form may be different, and a comparison of the traces (logs) on services similar but that use different technology is not immediate and sometimes impossible. Other well‐ known limitations arise when it comes to treating the very substantial volume often remarked (Honest et al., 2012) of these log files. Finally, as we could get usage data on a digital service, say x% for a category of users, how do one know if x% is a good rate or not? The main problem addressed here, is to demonstrate how the tool AGIMUS built in this context addresses as well technical issues but also, appears as collaborative tool by aggregating national usage data, to provide at the upper level a usage knowledge national system. We will discuss how this knowledge base could help policy makers in taking decisions taking account the AGIMUS national reference data for service consumption levels. The needs for interoperable infrastructure come as evident and at functional level, generic indicators must be sufficiently explicit to serve as an instrument for monitoring the functioning of the system. The data collection level in our work includes these works in the web usage mining field, however, digital services are accessible via authentication (users are known and declared into the information system), and we have to produce indicators at the national scale and not only on a few servers that is to ensure the need for coherence indicators. Furthermore, usual analyse of web data usage is self‐referred (Jansen, 2009) as performance indicators created are used historically for improvement of provided services. We propose here another way for analysing the indicators taking advantage of the national hierarchical construction to determine reference levels of usages of services. Within this disciplinary context, a solution to monitor cyberspace and to describe the behaviour of consumers (students, teachers, researchers… in our context) has been developed. The solution is extended also to provide national knowledge on the usage to construct a reference level to be targeted by cyberspace governance.

2. Application de gestion des indicateurs de mesure des utilisations de service (AGIMUS) Following a call for tenders launched in October 2009, the application was developed by a society in respect to the constraints presented before (Reymond and Dib, 2009). The application is called AGIMUS. It offers for each university the opportunity to build its own indicators measuring usage of the audited data services provided. Finally, the same application is used to produce aggregations and build trees of data collectors. The trees settled up can be as varied as the landscape of the ES (institutes, engineering schools, university, UNR, etc. see Figure 1 for the main intended tree construction) and the author publications for technical precision on the architecture.

3. AGIMUS indicators bundle As installed and the first logs processed, AGIMUS provides a bunch of preconfigured indicators. These initial indicators are relatively simple. We just describe and comment some of them and invite the interested reader to consult the annex of this article and Reymond and Dib (2010b) for a more complete description. The preconfigured set is split into three categories according to the purpose of AGIMUS use: monitoring and maintenance indicators, management and operational indicators specific to the collection system. AGIMUS provides also an interface to create internal indicator more specifics using the SQL language.

3.1 AGIMUS’ basic indicators We provide the complete description of predefined indicators coming within Agimus package in Annex 1. We remind hereafter the objective of such indicators combining the cyberspace level (level N) and the view on multiple cyberspaces (Level N+1).

421

David Reymond

Figure 1: Overall recursive implantation process within the 17 UNR, data flow and nomenclature for data aggregation process. Each level provides internal dashboard and feed upper level with normalized usage data

Figure 2: AGIMUS implantation into the information system. Transparent for the users, the data collector system operates in front of the cyberspace 3.1.1 Monitoring indicators Monitoring indicators provide information on the operational level and daily use. They are useful for planning updated applications, modifications to allow the selection of off‐peak periods to interfere as little as possible the user’s activity. These indicators detail the comparative degree of actual cyberspace use at level N. They are

422

David Reymond used to estimate a priori the degree of use of each service. This range covers indicators e.g. the number of connections, unique users daily and the number of operational services in the cyberspace.

Figure 3: Users dimensions in two levels mode. A generic anonymous profile and a sub‐decomposition of the student profile characterized using national nomenclatures 3.1.2 Governance indicators Governance indicators describe the services offered and service usage (by counting access), the average behaviour of users is expressed in session duration, and frequency of these uses. They are produced at an AGIMUS instance or aggregated to provide information on the actual use of services in comparable way. Institutions have also access to rates of connection counts and complete the analysis of these usages. Of course, each of the indicators mentioned above can be broken down into the categories of people, and for the student population, according to the dimensions that characterize them (see Figure 3). 3.1.3 Operating Indicators These indicators are intended to assist in Agimus implementation and validation of the audit procedure of the cyberspace. N +1 level is reconstructed also by summative aggregates. Those indicators monitors the population counts, the different URL (i.e. services) tracked, but also the patterns detected as a potential new service to track. For more details on these indicators and the method associated for piloting cyberspace deployment, the reader is referred to (Reymond and Dib, 2010a, 2010b). We focus hereafter on new composite indicators, not yet implemented.

3.2 Composite indicators At the governance level, a more synthetic view is expected to be proposed by AGIMUS (we plan it for next development patch). This view is composed by a special bunch of indicators that will provide a bi annual factual description of the cyberspace. At the first level of collect, this dashboard will describe the cyberspace richness and the usage degree. For the several aggregates levels, leading applications in usage degree or most consuming disciplines will compose also the dashboard. We describe hereafter such indicators. 3.2.1 Cyberspace richness We define it as the number of offered digital services within the functional categories developed to mask different kinds of cyberspace technical architectures (Reymond & Dib, 2010a). Figure 4 express the cyberspace range in term of digital services categories (Registrar, Library and resources, Information communication, etc.) where are classified in the AGIMUS configuring step the several services. This indicator expresses the coverture of the cyberspace offered and describes how many services are deployed in each category. In the example, we see that three services in the Registrar category are offered, while one service only in the e‐learning category is provided, etc. In order to appreciate also the degree of use of the cyberspace, we mix with the cyberspace richness the most used services using a threshold to make present in the graphic the number of services that are effectively used by a sufficient rate of the whole population. The threshold (arbitrary fixed in the fictive example in Figure 5) is applied to display the services used by more than 40% of the population. Thus, in the example above, we can

423

David Reymond see that the application range is covered by the cyberspace, the whole range seem to be used by an honourable rate of the population except within the Human Resource Management category. In the Information and communication category (this category encompasses webmail, chat, forums and blogs services) three digital services are offered. Two of these are used by more than 40% of the population. Such synthetic view may express at a time the cyberspace coverture but also its global degree of use.

Figure 4: The cyberspace richness expressed in term of a functional coverture in a range category and the number of digital services in each category

Figure 5: Cyberspace richness coupled with used services displayed using a threshold cut‐off

424

David Reymond 3.2.2 The leading exhibits indicators A second kind of synthetics views will be included in AGIMUS in order to highlight the cyberspace’s most used applications or most consumers’ profiles. For example, Figure 6 displays an extraction of the cyberspace services for a given utilization threshold rate for the entire population. The three main services appear in the fictive example. This indicator highlights the most used services for which a particular attention must be paid (availability, reliability). In‐depth analyse must integrate with this indicator the frequency of use, but also internal (to the university) differences for the several kinds of population for instance. Figure 7 shows the distribution of cyberspace usage per discipline in a multidisciplinary environment. This indicator allows examining further the interests of the various services offered to consumers under the disciplines. Less interested (in appearance, here in this fictive diagram Languages and Law) discipline should initiate targeted information campaigns, special support or in‐depth studies to understand better the poor attraction to cyberspace they seem to have. For the biggest consumers’ (here Science and Economy), such information should initiate in‐depth study to understand which new services would be needed or how can evolve the actual cyberspace. As disciplines are generally grouped in the same department this information offers a good way to show where to start for such qualitative and expensive studies. As AGIMUS proposes to customize the several indicators, other dimensions within the information system could be more pertinent to use in particular situations.

Figure 6: The leading digital services (fictive) appear with their respective usages rates (in percentage of the whole population) As the previous indicators focuses in inner situation, the AGIMUS aggregating process allow also building up comparative indicators that may help in evaluating the cyberspace. Before entering, the national view in the knowledge usage database projected, the combined form of the previous indicators, entering a light in‐depth overview, may provide composite indicators as presented in Figure 8. This indicator shows in a comparative display mode within several UNR the rate of use of applications (here the webmail). The rate of population (in y‐axis) is displayed for each year of study level (LMD followed by the number of years). In addition to the comparative aspect of highlighting the tools adopted by most people, this view could also highlights best practices information, support, and training tools that are provided besides the cyber infrastructure. As this information (how support is offered, how communication and accompaniment process are deployed besides the cyberspace) is not directly present, this could be a starting point for discussing best practices between the several providers.

425

David Reymond

Figure 7: Fictive comparison of discipline consumption of services. The rate of usage in y‐axis, and the disciplines present in the university in the x‐axis

4. National knowledge base as reference statistics Beyond informative indicators that can feed the creation of usage knowledge database at the national scale such as the most widely deployed applications (Figure 8), or the most commonly services used by students, the main interest would be to establish useful baselines to governance of digital services. As previously announced, as soon as AGIMUS delivers usage indicators on services deployed, actual use of those indicators is in self‐referred mode (Reymond and Dib, 2010b). That is actually the unique way to perform governance using the historical evolution of rates as the sole informational source. We state that this is due to the fact that we do not know what should be the "good" level of usage of a digital service (of course, one could target also a 100% rate, but a reference median level would inform where we are). Many questions arise in complement to this statement on what can be expected:

What is the average level of connection to a digital service?

What is the average frequency of use?

We state that providing this information will allow piloting the deployment of the cyberspace by knowing what should be targeted in term of usage. Interpretation of local indicators may also result easier and not only self‐ relative. To complement this, reference statistics should be also established for the different population categories segmented by level of study and disciplines as well. As, inner statistics can be biased by many social parameters, national level statics may inform on global trends in usage behaviour of students. This would facilitate furthermore the interpretations:

Should there be differences between disciplines?

Should there be differences in the level of studies?

Thanks to the basic functions of AGIMUS and its aggregative process, all the necessary data is gathered thought the different collectors. The establishment of this collective knowledge of the behaviour of students (that is the majority of the client population to the cyberspace), it will be possible to initiate a referee piloting process for the digital services and is straightforward connected to educational ICT system usage.

5. Conclusion The method of production of indicators associated with a guide method to pilot the cyberspace underlying this article is underpinned by research currently very active at the international level (Harley 2007; 2008; Nicolas & Rowlands 2010). Technical issues traditional in the web analytics domain (Castellano et al., 2013) are dodged by the collecting process of AGIMUS and the consideration of access usage only.

426

David Reymond

Figure 8: Fictive view (at the time of writing) on most deployed services in the national cyberspace using AGIMUS for monitoring usage AGIMUS is developed under the free software CECILL license, and is adaptable to a variety of architecture as a non‐intrusive way to audit access to web services. The authenticated context is used to group usage indicators by generic well‐known groups in the academic context (such as level of diploma or disciplines). Agimus is actually in version 1.5 and the deployment into the French universities cyberspaces is in process. Interoperability of data is ensured by standardization carried out by AGIMUS parameterization at the base of the collection process thanks to national referential (SISE, SupAnn). The reliability of measurements is guaranteed by the implementation of AGIMUS by the holders of the information on local specificities (Reymond and Dib, 2009, 2010a), the managers of the cyberspace that can choose if yes or not, access data should be aggregated for such or other service. This point avoids inconsistency frequent in widespread indicators. As the deployment of digital service isn’t straightforward (Gerbod and Paquet, 2001), AGIMUS offers a basic and standard dashboard to state the functional range of the cyberspace, monitors its activity and state its global usage rate. This first step‐indicators, allow a self‐referee piloting process, in a performativity mode for each services provided. The granularity of the indicators is linked on the natural groupings of academic profiles, and the standardized nomenclature for their description offers the possibility to refine this description and opens to in‐depth analysis. Observing local singularities in cyberspaces usages opens the way to targeted qualitative survey at low cost but more informative on usage, intensions and necessities of users. As the deployment of digital services is described as a must have for universities, those indicators are useful for policy makers in order to choose the digital tools, to appreciate their usefulness but also to begin process of accompaniment for users in a targeted and specific way. This help entering the local quality assessment of digital services deployed.As the AGIMUS standardized data open the way to compare different cyberspaces (Reymond and Dib, 2010b), we state also that this data may also feed a national reference database. Collecting and aggregating data at the national scale opens the way to build up some reference statistics that would be useful to start piloting process according to the median degree of usage that would provide this knowledge base. Only the widespread adoption of this solution within the academic institutions will allow producing reliable data on student’s behaviour. Agimus, associated with this national repository, opens up several ways to the implementation of functional additions, to improve ergonomics of service in line with actual and potential users, but also to make experiments deploying new applications. Exploiting Agimus aims in co building indicators in the cyberspace

427

David Reymond piloting process allow sharing costs by institution. As cyberspace are delicate and difficult to set (Gerbod and Paquet, 2001) but useful for universities (Jacquinot and Ficher, 2008); Agimus comes as a tools not only to satisfy internal indicator producing wider views as expected for Virtual Learning Environment (Dyson and Baretto‐Campello, 2003), but also it appears as a distributed tool to co‐construct a knowledge base on user’s consumption in order to keep virtual campuses (Brown et al., 2010) as up to date, attractive and useful. Further research, as soon as the deployment will provide data will address trying to dress up behaviour profiles, comparing global cyberspace behaviour and e‐learning skills (Reymond et al., 2012). National level is expected to aliment statistics for the observation of usages and digital services consumption by profiles, exhibit trends and help universities in the success of their cyberspace providing objectives.

Acknowledgments The author would like to thanks the many contributors to this development specially, Sandrine Twardy (Université de Toulon), Nicolas CAN (Université de Lille 1) and Julien MARCHAL (Université de Lorraine) for their precious technical advises and support.

Annex 1: AGIMUS predefined indicators AGIMUS comes with already settled basic indicators. They remain into three categories according to their usage objectives, conforming to the educational hierarchy level as described by Jacquinot and Fischer (2008, 20‐21): monitoring and maintenance indicators, management indicators and operating indicators. We describe them hereafter inviting the reader to keep in mind the possible variations in the several users’ characteristics presented in part III.2, but also with home settled dimensions allowed by free space in Agimus database. Monitoring indicators provide information about the daily operational services and their degree of use. They are expected to be used in a management plan: planning an update of applications, allowing low user incident modifications in choosing off‐peak periods, etc. AGIMUS acts at this level as many other systems offering the centralization of activity logs of all deployed digital services. AGIMUS also allows refining the indicators produced by splitting activity information on user’s categories. At the N+1 level, these indicators give a detailed comparative view of usage in institutions at N level. They can be used to estimate the degree of usage of each service and compare them. In the upper levels, the analysis should generate a best‐practice perspective (for instance regional), and exchange between managers methods or products that seems to be best suited for students and staff. Table 1: settled monitoring indicators in AGIMUS E‐service operator (N level)

Services aggregate (N+1)

Number of uses Effective daily users Number of operational services

Number of uses Effective daily users

In table 2, we present the governance indicators that are used to describe the services and categories of service in order to outline the average behaviour of the users. Behaviour is simply expressed in terms of the duration of a session and the frequency of connection. Whether they are produced for a single institution or, by aggregation, for several institutions these indicators provide information about the effective use of services. Locally, the institution can use the raw connection rates to complete the analysis of usages and split it into inner group’s differences. Table 2: settled governance indicators in AGIMUS Digital service operator (level N) Digital service covering Average session duration Average frequency of use Raw connecting rates Types of browsers

Aggregated level (N+1) Category view Categorized digital service covering Average session duration Average frequency of use Raw connecting rates

Table 3 describes the operating indicators specific to the AGIMUS device itself. These indicators are designed to assist in its implementation and the validation of the audit process of digital services. At N+1 level the data is reconstructed by a summative aggregation. In this indicator, appears the population numbering that is used to calculate rates for all indicators of penetration estimation within the population. The numberings here are as important as the dimensions settled in the database (number of students for each level of study + number of student for each discipline + number of students for each registrar mode and so on).

428

David Reymond Table 3: AGIMUS settled operating Indicators Indicators URL detected Digital audited services Population numbering

Definition Allows the identification of new applications List of web applications declared and audited The number of the different population is used when calculating rates.

References Baty, P. (2011). Change for the better. Retrieved November 21, 2012, from http://www.timeshighereducation.co.uk/world‐ university‐rankings/2011‐12/world‐ranking/methodology Boukacem‐Zeghmouri, C., (éd.). (2010). L’information scientifique et technique dans l’univers numérique : mesures et usages, ADBS éditions, 319 p. Brown, M, Paewai, S and Suddaby, G. (2010) “The VLE as a Trojan Mouse: Policy, Politics and Pragmatism” Electronic Journal of e‐Learning Volume 8 Issue 2, pp. 63–72, available online at www.ejel.org Castellano, G., Fanelli, A.M. and Torsello, M.A. (2013) « Web Usage Mining: Discovering Usage Patterns for Web Applications ». Advanced Techniques in Web Intelligence‐2. Éd. by Juan D. Velásquez, Vasile Palade, & Lakhmi C. Jain. vol. 452. Springer Berlin Heidelberg. P. 75 104. Studies in Computational Intelligence. Chen, M.S., Park, J.S. and Yu, P.S. (1996) “Data mining for path traversal patterns in a web environment”. In Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96). IEEE Computer Society, Washington, DC, USA, p. 385‐392. Comité Réseau des Universités (CRU) (2009), French Higher Education Institutions directory recommendations : SupAnn, website visited december 2012 : https://www.cru.fr/documentation/supann/2009‐en/index Conférence des Présidents d’Université – CPU‐ (2005) Définition d'un cadre de cohérence pour la gestion de la scolarité dans le supérieur – rapport final, Ministère de l’Education Nationale, de l’Enseignement Supérieur et de la Recherche, État des lieux V2.2 du 6 décembre 2005, Ed. SOLSTIS, Paris. Retreived december 2012 from : http://www.cpu.fr/ uploads/tx_publications/Cadre_de_coherence_scolarite‐Rapport.pdf Cooley, R., Mobasher, B., Srivastava, J. (1997) Web Mining: Information and Pattern Discovery on the World Wide Web. Proc. IEEE Intl. Conf. Tools with AI, Newport Beach, CA, pp. 558‐567. Dyson, M., Barreto Campello S., (2003) ‘Evaluating Virtual Learning Envirronment’. The Electronic Journal of e‐Learning (EJEL), p. Vol 1, N°1; Feb, p. 11–20, available online at www.ejel.org Gerbod, D. and Paquet. F. (2001) Les clefs de l’e‐administration, Pratiques d’Entreprises, Editions Management et Société. Harley, D. (2007) “Use and Users of Digital Resources: A survey explored scholar's attitudes about educational technology environments in the humanities”. EDUCAUSE Quarterly N°4. Harley, D. (2008) “Why Understanding the Use and Users of Open Education Matters”. In Opening Up Education: The Collective Advancement of Education through Open Technology, Open Content, and Open Knowledge, edited by Toru Iiyoshi and M.S. Vijay Kumar. Cambridge, MA: MIT Press. Hazelkorn, E. (2008) « Learning to Live with League Tables and Ranking: The Experience of Institutional Leaders ». Higher Education Policy vol. 21 N° 2. p. 193‐215. Jacquinot G. and Fichez E., (2008) L'université et les TIC, Collection: Perspectives en éducation et formation, De Boeck Université,. 328 p. Jansen, B. J. (2009 ) “Understanding User‐Web Interactions via Web Analytics”, N°6 in Synthesis Lectures on Information Concepts Retreival and Services, Morgan & Claypool Publishers. Martin, M. and Sauvageot C. (2011). Constructing an indicator system or scorecard for higher education. A practical guide. International Institute for Educational Planning, UNESCO. Paris, France: Bertrand Tchatchoua. Print. Higher Education. Ministère de l’Education Nationale, de l’Enseignement Supérieur et de la Recherche (2005), BCN, Base Centrale des Nomenclature sur le web « BCN web » : www.infocentre.education.fr/bcn Nicholas, D, Huntington, P, (2008) “Evaluating the Use and Users of Digital Journal Libraries”. In: Digital Libraries, Ed. Papy, F, London: ISTE Wiley Nicholas D, Rowlands I. (2010) “Digital consumers: case study virtual scholars. A deep log analysis“. In L'information scientifique et technique dans l'univers numérique : mesures et usages. ADBS, pp 27‐42. Reymond, D. and Dib, K. (2008), « Indicateurs d’usage des services numériques déployés au sein des universités numériques en région », dans Actes du colloque international "l’Université à l’ère du numérique" (CIUEN 08), Bordeaux. Reymond, D. and Dib, K., (2009), « Vers une interopérabilité de la mesure d’usage des ENT : enjeux, objectifs et méthode », Intelligence collective et organisation des connaissances, in actes du 7e Colloque du Chapitre français de l’International Society for Knowledge Organization (ISKO), M. Hassoun et M. El‐Hachani (Eds), Ensib, Lyon, p. 287– 293. Reymond, D. and Dib, K., (2010a), « Mesure d’usage et organisations multi‐échelles : indicateurs et méta indicateurs d’usage des ENT.», dans colloque international « l’Université à l’ère du numérique » (CIUEN) jun, Strasbourg

429

David Reymond Reymond, D. and Dib, K. (2010b), « Mesures d’utilisation : vers un pilotage intelligent des services numériques de l’enseignement supérieur », In Actes du Colloque International AFRICAMPUS, Les usages intelligents des technologies de l’information et de la communication dans la réorganisation universitaire, Dakar Sénégal. Reymond, D., Dib, K., and Kromm H, (2012) “Data‐mining and indicators Conference: Collaborative Learning & New Pedagogic Approaches in Engineering Education, Morocco 17‐20 April 2012, p. 1‐10, DOI 10.1109/EDUCON.2012.6201068. Srivastava J., Cooley, R., Deshpande, M. and Tan, P.N. (2000). Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explor. Newsl. 1, 2 (January), 12‐23. DOI=10.1145/846183.846188 Waltman, L.; Calero‐Medina, C.; Kosten, J.; Noyons, E.C.M.; Tijssen, R.J.W.; Eck, N.J. van; Leeuwen, T.N. van; Raan, A.F.J. van;Visser, M.S.; Wouters, P. (2012) « The Leiden Ranking 2011/2012: Data collection, indicators, and interpretation ». CoRR abs/1202.3941 23 p.

430

Functional Architecture of a Service‐Oriented Integrated Learning Environment Danguole Rutkauskiene1, Rob Mark2, Ramunas Kubiliunas3 and Daina Gudoniene1 1 Department of Multimedia Engineering, Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania 2 Centre for Lifelong Learning, Faculty of Humanities and Social Sciences, University of Strathclyde, Glasgow, UK 3 Department of Software Engineering, Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania danguole.rutkauskiene@ktu.lt rob.mark@strath.ac.uk ramunas.kubiliunas@ktu.lt daina.gudoniene@ktu.lt Abstract: A wide variety of tools and systems are used in the development and implementation of learning activities through e‐Learning. While there are tools in learning management systems which can be used to implement a wide range of learning activities and develop a rich virtual learning environment, this is sometime insufficient and learning activities are also usually carried out not only within the learning environment, but also outside (for example, searching for information online, communication in social networks, self‐reflection in blogs, retrieval and presentation of information in academic information systems, etc.) In this paper we argue that each of these activities should be integrated into a successive learning process through the development of a more integrated learning environment. A range of technological solutions for the development of integrated e‐Learning environments already exists in the literature. However, the most appropriate solutions require further research and the development of integrated learning environments are still in the early stages. The aim of this paper is to present the functional architecture of the integrated learning environment that enables the implementation of a successive learning process using advanced learning methods. We will review technological solutions for the integrated learning environment based on existing academic literature. This will include an analysis of tools necessary to implement learning activities using advanced learning methods: personalised learning processes, and social communication and collaboration. The paper will also present the functional architecture of a service‐ oriented integrated learning environment. The architecture is flexible and reliable and can be easily extended. The development of the integrated learning environment is relevant for all educational institutions providing training services and seeking to improve the quality of learning. Keywords: e‐learning, learning activities, learning processes, e‐Learning tools, integrated learning environments

1. Introduction A variety of tools and systems are used in the implementation of learning activities through e‐Learning. Hortons (2003), when writing about e‐learning technologies note that “The most painful question we get as consultants is – “What tool should I use for e‐learning?”. What is painful about the question is that it shows the questioner has been misled to believe there is one single tool that does everything everybody needs to do to create, host, and access e‐learning. Successful e‐learning projects may require dozens of software products chosen from hundreds of candidates sprawling across several categories.” After ten years of development of e‐ learning technologies, the situation is basically the same. There are different categories of tools and systems used in e‐learning: learning and learning content management, communication and collaboration, live learning and assessment, etc. They can also be categorised according to the possibilities of implementation of curriculum (realisation of learning events: Imitate, Receive information, Exercise, Explore, Experiment, Create, Self‐reflect, Debate); technological properties (e.g. synchronous, asynchronous, web based, PC application, mobile app, open source, free service); application domain (language learning, intercultural competences, ICT skills, time management skills, study habits skills, etc.). However, independent on the purpose or functionality, all tools and systems are used to implement a virtual learning environment (VLE). And the main aim is that a VLE should “reflect the nature of the discipline by providing a well‐designed; visually stimulating environment that genuinely supports the real world learning environment” (Malins and Pirie, 2004). Modern learning management system (LMS) integrates various tools which can be used to implement a wide range of learning activities and develop a rich VLE. However, this is sometimes insufficient and learning

431

Danguole Rutkauskiene et al. activities are also carried out not only within the learning environment, but also outside it. This includes searching for information online, communication in social networks, and self‐reflection in blogs, retrieval and presentation of information in academic information systems. Besides, the personalised learning and social communication as well as collaboration are now considered as advanced learning methods, and the development of tools implementing such methods is in the focus of attention. As a result, educators and learners from time to time supplement the learning process with tools and systems, which are separated from LMS. This can be a problem, because using them separately it is difficult to implement successive learning processes in VLE, in which the learning activities are related with the learning outcomes of students, and the learning experience is shared with each other. The VLE has to be even more flexible, personalised and integrated. “Integrated VLE implements full e‐learning process cycle: management, e‐Learning content preparation, e‐Learning content delivery and learning” (Targamadzė et al, 2005). A range of technological solutions for the development of integrated e‐Learning environments exists already in the literature. However, the most appropriate solutions need further research and the development of integrated learning environments is still in the early stages. The aim of this paper is to present the functional architecture of the integrated learning environment (ILE) which enables the implementation of a successive learning process using advanced learning methods: personalised learning process, social communication as well as collaboration. The model of the system used to implement the ILE is based on the service‐oriented architecture. It allows making a service‐oriented ILE with the ability to implement effective services and integration solutions. The architecture is flexible, reliable and can be easily extended. The aim of the research of this paper is to select tools for integration into the educational platform which implements e‐Learning design. The tasks of the paper are: to examine and review the tools which are necessary to implement learning activities using advanced learning methods; to analyse the functional architecture of a service‐oriented integrated learning environment. The methods applied for the research: the review of literature, the analysis of tools and systems.

2. Overview of the tools for e‐Learning Modern tools and systems are necessary to implement an e‐Learning strategy. However, as Rosberg argues (2007) “any e‐Learning strategy must include methods for designing and deploying learning solutions, change management, communication planning, performance support, and knowledge management services and technologies”. Technologies only enable learning methods, which are the means of learning process. Therefore, analysing tools and systems for e‐Learning we have to concentrate on learning activities first rather than on the technologies. On the other hand, as Laurillard (2002) notices “We may not have an agreed set of characteristic forms of effective e‐Learning, but it is possible for the educational community to identify some effective existing learning activity models. These would embody good design practice of a kind that might impose requirements on the underlying e‐Learning architecture”. By analysing the essential characteristics of a range of proven learning activities, we can generate a set of requirements for the architecture of ILE. Almost ten years ago Downes (2005) wrote that “e‐Learning takes mainly the form of online courses. From the open resources distributed online to the design of learning to the offerings found from colleges and universities everywhere, the course is the basic unit of organisation”. This is still the same situation. The main learning technology used by educators is LMS which enables to manage and delivery online courses. Several years ago, the Moodle (Modular Object Oriented Dynamic Learning Environment) system began to be using as open source web‐based learning management system in Lithuania replacing the commercial Blackboard system which was used previously. Moodle is also used at Kaunas University of Technology (KTU) to assist teachers developing and managing learning courses online. However, the system is also successfully used not only in higher education but also in vocational and general education. Moodle has a simple and convenient user interface. Courses can be sorted by different categories and the search may be performed within. The

432

Danguole Rutkauskiene et al. installation process is simple and installed systems can be supplemented with new modules. The Moodle system as one of learning management systems is therefore an important and necessary tool for ILE. Another tool which could be selected for ILE is a ViPS (a Video Presentation System) system. It has been developed at KTU and is used for recording and broadcasting conferences or lectures. Teachers can broadcast lectures or seminars online to students. At the same time a streaming video is saved and stored online on the server of the system for later viewing. ViPS assigns the area in which the records are stored. Records are always in one place, so they are easy to reach for internet users. This is very convenient because the area can be named like a learning module. Besides, users have the ability to keep track the selected area and be aware that there a new records which have not been viewed. The information about the area could be transmitted to the ILE as a service of data. Also, other possible sources of information might be: consumer video preview, preview date; related video to preview; related areas suggestions; the most popular or newest video information; current areas of information about the new record. The video broadcasting and recording system is an essential e‐Learning system where the teacher uses the lecturing activity. Social communication as well as collaboration is now the dominant activity online. Social software enables advanced learning methods which are related with interaction and collaboration. The social networking software Elgg could be used in the ILE as social communication and collaboration system. Elgg has already been used in various e‐Learning projects and courses as a tool to assure successful ICT based study process. It can also be integrated with Moodle system which has some social tools, but cannot compete with such software which is based for special purposes. Moreover, the ILE could include the open source content management system (CMS) Drupal. As an addition the CAS (Central Authentication Service) service of Drupal CMS could act as a central user authentication and log‐storage base service, which supports the SSO (Single Sign‐On) protocol. Using the SSO, all mentioned systems could be integrated in user authentication level. This is a very important as logging into several different systems during the learning processes can be an irritating and distracting activity. For effective use of digital learning material it is essential to have it as learning objects and organise them in the right way. So they can be easier to use as well as being used again to create new courses or complemen the old ones. Content can be reused in different contexts, not only in original, in which it was authored because of the learning object technology (Redecker et al, 2009). Re‐use is important due to the fact that authoring of high quality e‐Learning material is expensive, and attempts to lower costs of e‐courses design and deployment while keeping high quality standards is desirable (Rutkauskiene and Gudoniene, 2010). The best way to stimulate the reuse of learning objects is to provide convenient tools for e‐courses authoring, which would aid the idea of simple yet effective learning object reuse. The best tools are those, which are easily embedded into the process of e‐Learning (Targamadze et al, 2010) and accepted by e‐learning participants, so not only e‐course authoring is important, but also the process of acquiring and evaluating knowledge, that was received by students from learning objects. Integration between learning management system and learning object repository could allow easy access for institution users to e‐learning material search, creation, annotation and various modifications not requiring any special knowledge of web technologies (Fertalij et al, 2010). As far as users are typically already acquainted with learning management system in their e‐Learning courses, there would be much easier to introduce the learning object repository as an extension to LMS, but not as separate tool. The reviewed tools and systems which are used in e‐Learning and could be used in the architecture of ILE meet particular e‐learning activities. However, the architecture should not make the finite set of tools (Targamadzė et al, 2005). It should be open and flexible for integration of any other new tools or systems.

3. Service‐oriented integrated learning environment 3.1 Information system As the e‐Learning market is full of tools and systems for any learning activities the ILE should be based on them. To create a new single system with a full set of learning tools for e‐Learning is practically impossible. The e‐Learning is actively researched and developed. Therefore, there will always be developed any tools or systems for any new learning activities. However, “the integration of the technologies that form the integrated

433

Danguole Rutkauskiene et al. learning environment is key to providing students with easy of use and seamless navigation” (Knight and Bush, 2009). Authors point to some technical issues that need to be addressed in the integration of the technologies. First of all students should be provided with a single ‘sign‐on’ so that they only have to login once and can then easily access each technology. Also, it should be possible grouping students across technologies. Group work forms are important part of the learning process in ILE. To make a single sign‐on is not a very great challenge. For instance, KTU already uses a common login system (login.ktu.lt) that provides information about the person (name, address, email address, user code, etc.) trying to connect to any of the single login harmonised system. It is a unified login system that enables connection to the e‐Learning, social networking and other systems. However, Dillenbourg (2000) states that “a higher degree of integration is reached when applications share or exchange data structures”. According to the author, technical integration should support the pedagogical integration. This means, that activity of learner in integrated applications should be visible for both or all integrated applications on the purpose to inform learner about various useful information or activity which could be missed without integration. Also, the integration helps teacher to collect the data about the activity of students in separated applications. Therefore, Tin Lee (2005) describes the ILE as environment, which “allows teachers to progressively assess their student’s learning and to design different activities for different students according to their ability levels”. Such integration could be done through the information system (IS) which is responsible for a user friendly navigation and data interchange between the integrated tools and systems. It is something like client‐server system which has been described by Jesshope et al (2000) discussing about technology integrated learning. Such system has “a central database server, a local database server and a set of tools that are used by students, teachers and administrators supporting all of the above approaches. The tools are integrated using information in the database, using application and web‐based clients” Jesshope et al (2000).

Figure 1: General social networking platform According to the previously reviewed e‐Learning tools and systems in this paper, the integrated IS mediate between e‐Learning systems, video presentation system and social networking system, which is used for social communication and collaboration. However, IS also integrates social tools which are used in the e‐Learning systems. IS provides the system integration service on the purpose to implement integration of tools and systems. And also includes the personalised information service on the purpose to implement pedagogical integration. As the social networking systems usually are implemented on the open source platforms and may

434

Danguole Rutkauskiene et al. be easily extended, one of the ways to make integration of tools and systems may be the same social networking system which then together with integration services would compose the General social networking platform (Figure 1). In such a way the general social networking platform is responsible for integration and includes: social networking system with an e‐portfolio of learners; system integration, metadata and personalised information services; social networking and recommendations plug‐ins which are added to the integrated tools and systems. The platform also enables the integration of social networking tools into e‐learning content and learning management systems. The activity of learners using social networking tools is gathered as artefacts which reflect the interests of learners. For instance, the meta‐information as artefact from e‐Learning system is the information about courses, modules and tests in which the learner takes part and receives certificates. The meta‐information as artefact from video presentation system is information about the multimedia learning objects – video presentations which were created by teachers or previewed by other learners or conferences in which take part teachers and learners. E‐portfolio is used for organised accumulation of such artefacts. The rich e‐portfolio is intended to reflect better the interests of learners and allows the system to provide more precise personalised services. Arrows in the diagram of the General social networking platform shows the flow of the information policies (Figure 1). The integration service calls the metadata service‐contained systems. The metadata service extracts the meta‐information about the interests of leaners (artefacts) from the database of the integrated system through its application programming interface (API). The metadata service transmits the extracted meta‐ information to the system integration service. The system integration service transforms the information and transmits it to the e‐portfolio. In the e‐portfolio the information about the activity and interests of learners is collected. The personalised information service receives a query from social networking and recommendations plugins which are responsible for making personalised recommendations based on the portfolio of the learner. The personalised information service takes the information from the e‐portfolio and transmits it to social networking and recommendations plugins. In such a way the opportunities for social interactions are expanded. Other useful and actual tools and systems could also be integrated into ILE using social networking and recommendations plugins and metadata services.

3.2 General architecture model Using various web services for communication between integrated systems we design the IS which architecture model is based on service‐oriented architecture (SOA). This architecture enables development of a flexible system with the ability to implement the effective services and integration solutions. As Woods and Mattern (2006) write: “via web services business software applications can interoperate flexibly over internet protocols and standardized interfaces”. The SOA technology is already used frequently: “looking back at a history of distributed communication standards such as DCOM, CORBA or RPC, service‐orientation is not a new architectural pattern in itself” (Alonso et al, 2004). However, Leyking (2007) notices that “the commoditization of internet communication and the strong demand for flexible distributed software systems, though, have pushed industry initiatives to develop web service standards, some of which have reached a level of maturity in the meanwhile”. Therefore, when Zomeren et al (2008) analyse the possibilities of VLE integration using SOA, they state that “a Service Oriented Architecture makes it possible to integrate these infrastructures without having to discard many legacy applications, which would mean a great loss of capital as well as loss of managerial support”. Although there are many possible variations, the most common web application is a multi‐layer structure. The first layer is the user interface that allows the user to perform the desired commands. The second layer is the logical layer, which is coordinating processes, performing calculations and processing and transmission of data between the other two layers. The third layer is a database management system, which contains all the necessary information. The client programme provides the user's query to the logical layer modules. The logical layer modules in the database find all the relevant information, which can then be sent back to the second layer, which is processed and presented to the user in the first layer. The general architecture model of IS is structured of several logical levels: user interface, operational logic and data (Figure 2). In the user interface (presentation) level users and interface of information systems is realized.

435

Danguole Rutkauskiene et al. The interface has necessary functions according to the system requirements. The system provides these user roles: institutional participants (companies and high schools) individual participants (students, mentors and teachers) and also the system administrator's role for management of all the other roles and content. Basically all employees and partners of the institution should be provided with the necessary role in IS. The user identification and analysis including impact evaluation are usually performed in the process of integration planning at early stages.

Figure 2: General architecture of IS The data layer implements all data management, sanctioning, monitoring, archiving, and storage components. The operational logic level consists of all the system components which are required for maintenance of the user interface and processes of the system performance. This level is responsible for operational and relationship integration and sends data (exchange) between the data and the user interface levels. The data flow between the logical levels is implemented omitting the secondary layer. This means that the presentation and interface‐level data is managed in accordance with the operational logic level modules. The integration process should be carefully planned. Therefore, the project of the reform of the IT sector was performed at KTU institution. According to the analysis of the current situation the future trends of the development of this sector have been proposed and the recommendations for the necessary reform of the technical and organizational infrastructure of the university have been introduced. The new main webpage of KTU and integrated webpages of faculties have been developed. The virtual learning environment Moodle2 has been installed. The strategy of IT infrastructure management has also been prepared (the development of the new structure of KTU Information technology centre, the development of the integrated system of information systems, the installation of the common management system of KTU webpages) and the consolidation as well as the use of IT resources has been implemented (the implementation of the single data centre conception, the foundation of the reserved data centre, the development of cloud technology).

3.3 Functionality and characteristics The architecture of IS and technical solutions should ensure the reliability of the system, as well as its ability to increase capabilities (scalability). Also the IS should function reliably. This reflects on the concept of dependability. The IS – is a generalized system of property which includes the following system characteristics such as availability, reliability, maintainability, safety and integrity. Therefore, the proposed architecture of IS is developed according to various requirements:

IS should be implemented as a modular system in accordance with the principles of SOA architecture;

three‐tier client‐server architecture and the flexibility of the system should allow the integration of the IS with other systems;

436

Danguole Rutkauskiene et al.

user interface should be convenient, developed using HTML and AJAX technology with accordance to the World Wide Web Consortium (W3C) standards and guidelines;

IS should have an attractive design, based on the best usability (called usability practices).

The interface of IS should also be flexible, robust and easily configured from the user's working environment. It should ensure that users will be able to start working with a new system easily. The flexibility of optimizing the processes should allow continuously improve and adapt the IS according to changing laws, regulations and needs. The additional software in the workplace of user should be free and user could be able to download the latest versions of the software directly from the website of the distributor. The software should be provided with the illustrated installation instructions. This has actually been planned in the strategy of IT infrastructure management and use. The IS should also implement the functionality for storing and analysing performance of data objects change history. It should be possible easy to view and record the user's system actions as well as implemented security during data transfer and data protection from unauthorised use and / or modification of any such modification tracking.

3.4 Social communication and collaboration Social software applications include communication / collaboration tools and interactive tools. Communication tools typically handle the capturing, storing and presentation of communication, usually written but increasingly including audio and video as well. Interactive tools handle mediated interactions between a pair or group of users. According to recent trends, most of the tools that used to be desktop‐based software are moving over to the web. The web is gradually becoming a social environment for communication and collaborative content creation.

Figure 3: Integration of external IS There are different categories of the tools (Figure 3) to be used for different aims and possibilities of e‐learning implementation (realisation of learning events: Imitate, Receive information, Exercise, Explore, Experiment, Create, Self‐reflect, Debate); technological properties (e.g. synchronous, asynchronous, web based, PC application, mobile app, open source, free service); similar or related tools; application domain (language learning, intercultural competences, ICT skills, time management skills, study habits skills, etc.). According to the previously reviewed e‐Learning tools and systems in this paper, Drupal CMS, Moodle, Elgg, and simulation gaming system are integrated with each other in both functional and user levels. The CAS service of the Drupal CMS allows it to act as the central user authentication and log‐storage base. The SSO (Single Sign‐On) protocol is used to connect for user authentication in Moodle, Elgg, and other systems. The API of Moodle integration

437

Danguole Rutkauskiene et al. service package allows the Drupal CMS to perform Moodle functions – creation of applications, users, groups, etc.

Figure 4: Integrated IS The integrated IS is designed to mediate between the e‐Learning and social networking systems. The integration includes the system integration and the personalised information services. The system integration service applies to the metadata service that brings together existing systems and protocols in the metadata of the information received about the student artefacts e‐portfolio. Metadata services use e‐Learning environments application programming interface from the database to extract the learner's interests in defining the content and transfer it to the integration services company (Figure 4). The proposed integrated IS implements the service‐oriented ILE which enables technological and pedagogical integration using various e‐ Learning and social networking tools for the interactive learning, communication as well as collaboration.

4. Conclusions The e‐Learning market is full of tools and systems for any learning activities and the ILE should be based on them. However, technologies are used for learning methods not learning methods for technologies. Therefore, analysing tools and systems for e‐Learning we have to concentrate on learning activities first rather than on the technologies. Or we should analyse the e‐learning tools and systems which are already used for teaching and learning and should also be included in the ILE. The analysis showed that the ILE should include the learning and learning content management systems, the video conferencing and presentation system, as lecturing is still important for the formal learning, the social online communication and collaboration system, as social communication as well as collaboration are now considered as advanced learning methods, the simulation game environment, as playing games is very attractive activity for learners. The integrated technologies enable a range of individualised constructive learning strategies and social skills acquired through constant communication, active sharing of knowledge and experience, joint activities in various groups, teamwork and training (learning) environments and social networks, with development and evaluation of the work performance. The proposed functional architecture of a service‐oriented ILE enables the development of a flexible ILE which has many services useful for promoting learning activities. However, the ILE is open to integration of other tools and systems which can be developed or adapted later for other advanced learning activities. It is very important, because to create a new single system with a full set of learning tools for e‐Learning is practically impossible. The e‐Learning is actively researched and developed. There will always be developed any tools or systems for any new learning activities. Therefore, the general architecture of ILE is based on three‐tier client‐ server architecture and is structured of several logical levels: user interface, operational logic and data. This enables the flexibility of the system and the possibility to integrate other e‐learning tools and systems.

438

Danguole Rutkauskiene et al.

Acknowledgements This paper has been partially supported by EUREKA scientific programme ITEA 2 Call 4 09020 (European Structural Funds project No. VP1‐3.1‐ŠMM‐06‐V‐01‐003, FFCC, VICAM).

References Alonso, G., Casati, F., Kuno,H., Machiraju, V. (2004) Web services. Concepts, architectures and applications, Springer. Dillenbourg, P. (2000) “Virtual learning environments”, EUN Conference 2000, Workshop on Virtual Learning Environments. Downes, S. (2005) „E‐learning 2.0“, eLearn Magazine. Education and Technology in Perspective. Fertalj, K. Hoic‐Bozic, N., Jerković, H. (2010) “The Integration of Learning Object Repositories and Learning Management Systems”, Computer Science and Information Systems, No. 7, pp 387‐407. Horton, W. and Horton, K. (2003) E‐Learning Tools and Technologies, Wiley Publishing, Inc. Jesshope, C., Heinrich, E., Kinshuk (200) “On‐line Education using Technology Integrated Learning Environments”, Massey University, New Zealand; Kemppainen, P.: Network Intelligence: A Paradigm for the Service and Networking Convergence towards Universal Communications. IEC Annual Review of Communications. Knight, A., Bush, F. (2009) “The development of an integrated learning environment”, Proceedings ascilite Auckland 2009. Laurillard, D. (2002) Design tools for e‐Learning. Unitec. Leyking, K. (2007) “Service‐oriented Knowledge Architectures – Integrating Learning and Business Information Systems” EC‐ TEL 2007 PROLEARN Doctoral Consortium 2nd European Conference on Technology Enhanced Learning, Vol. 288. Malins, J., Pirie, I. (2004) “Developing a Virtual Learning Environment for Art & Design: A Constructivist Approach”, European Journal of Higher Arts Education. Peters K. (2007) “M‐Learning: Positioning educators for a mobile, connected future”, IRR ODL, Vol 8, No 2, pp 66‐75. Redecker, C., Ala‐Mutka, K., Bacigalupo, M., Ferrari, A., Punie, Y. (2009). Learning 2.0: the impact of web 2.0 innovations to education and training in Europe, Joint Research Centre, Institute for Prospective Technological studies. Rosberg, M. (2007) eLeanring Strategy, The e‐Learning Guild. Santa Rosa. Rutkauskiene, D., Gudoniene, D. (2010). "e‐Learning: Trends and Challenges, Learning Community and the Second Generation of the Web 2.0 Technologies. Mathematics and Informatics, pp 67‐75. Targamadzė, A., Balbieris, G., Kubiliūnas, R. (2005) „The new generation of virtual learning environments in Lithuania“, Information Technology and Control, Vol.34, No.3. Targamadze, A., Petrauskiene, R. (2010) “Impact of Information Technologies on Modern Learning”, Information Technology and Control, Vol.39, No.3, pp 169‐175. Tin Lee, K. (2005) “Teachers using the ILE to cater for individual learning differences in Hong Kong primary classrooms”, Technology, Pedagogy and Education, Vol. 14, No.3. Woods, D., Mattern,T. (2006) Enterprise SOA: Designing IT for business innovation, O'Reilly. Zomeren, B., Klein, J., Portier, S., Blom, R. (2008) “Integrating virtual learning environments using a Service Oriented Architecture (SOA)”, EUNIS 2008 VISION IT ‐ Vision for IT in higher education.

439

Using Social Network VKontakte for Studying Sociology Daniyar Sapargaliyev1 and Assel Jetmekova2 1 International Academy of Business, Almaty, Kazakhstan 2 S.Seifullin Kazakh Agro Technical University, Astana, Kazakhstan dsapargalieff@gmail.com jetmekova@hotmail.com Abstract: This paper describes an empirical study, which analyzes the quantitative data obtained from a survey by students at Kazakh Agro Technical University in Astana. The aim of our survey was to determine how the use of the social network VKontakte can help in studying the Sociology course. We studied the experience of use of social media in education systems of the former Soviet Union states. The results showed that the use of social networks for learning is still at an early stage of development. In September 2012 we created an educational group ‘Sociology (Astana)’ on the social network VKontakte. This social network is the most popular among students of Kazakhstan. We created five topics for seminars. After completion of the Sociology course, which lasted four months (from September to December 2012), we conducted a survey among 100 students. The survey data demonstrates that about a third of students had already used social network VKontakte for learning. Many students use not only personal computers for access to the educational group ‘Sociology (Astana)’ but also use mobile devices, in particular mobile phones. Students were actively involved in network discussions; they regularly added new comments, videos and other learning materials. Students noted that participation in the educational group VKontakte helped to develop a good relationship among classmates. The results of our study can be used by educators to further research work devoted to the use of social networks in Kazakhstani universities. Keywords: social network, VKontakte, students, Kazakhstan, sociology, mobiles

1. Introduction In recent years, we can observe the process of transformation in education systems in the former Soviet Union states (Russia, Kazakhstan, Ukraine and others). This is primarily due to the desire of the governments of these countries is gradually moving to global standards in education. However, the changes in education systems depend on regional characteristics. It is important to note the rapid growth of interest in the use of social media in higher education in Russia and Kazakhstan. In this region, the most popular social media are beginning to be used in the learning process of universities. One of the most used social networking is VKontakte. According to Wikipedia (2013), ‘VKontakte’ is the largest Russian language social network that was established in 2006. This social network is the second most popular site in Russia and Belarus, the third one in Ukraine and the fourth in Kazakhstan. VKontakte originally positioned itself as a social network of students at Russian universities. The daily audience of VKontakte is more than 43 million people in 2013. Many educational institutions are trying to use the enormous potential and prevalence of the social network VKontakte. In this article we will look at examples of successful use of social networking in higher education in Russia and in other Post‐Soviet countries. Over recent years a number of studies have examined the phenomenon of the use of VKontakte in education. Many scientists agree that VKontakte has the potential to become an instrument of social transformation, knowledge and the formation of scientific groups (Libshner, 2010; Kribel and Shobukhova, 2012). Shipicin (2011) for example, looked at the phenomenon of social networking in modern Russian culture. The author considered the functional characteristics and the role of VKontakte in everyday student life. Other researchers Gerkushenko and Sokolova (2012) concluded that this social network can provide the basis for development in modern Russian science, education and society. Some authors, for example Feshenko (2011) and Zhukova (2011), suggested that VKontakte has good educational potential for Russian higher education. However, as Alexandova (2012) persuasively argued in his highly recommended study “…students keep in touch with educators in social networks and actively engaged in self‐development.” The author identified potential for the use of social media in Russian higher education. Moreover, as the author points out, the use of VKontakte allows learners to develop technological skills, creativity and interaction with international students. Some scientists studied the possibility of VKontakte and its use as a new learning platform (Kovalenko, 2011; Olshevskaya et al., 2011). For example, Zakurdayev and Lavrov (2012) conducted an experiment for developing a distance learning system based on the software interface of the social network VKontakte. The authors

440

Daniyar Sapargaliyev and Assel Jetmekova studied the mechanism of interaction of VKontakte with external applications and developed the prototype of a distance learning system based on this social network. Bikunov (2012) in his study compares the possibilities of the course management system Sakai and the social network VKontakte in studying the course ‘Information Management’ for first‐year students. The important result of experiment was the absolute preference of students for the virtual group VKontakte. Students were more likely to have used VKontakte for educational discussions, which they already used to communicate in their daily lives, rather than the separate website Sakai for the learning process. The students mentioned that the educational group VKontakte ‘Information Management’ supported active contact with a lecturer. Also, many scientists are investigating certain pedagogical and psychological aspects of the use of VKontakte in the modern university (Babin and Redko, 2011; Morgunova, 2011). For example, Shchekoturov (2012) examined the students’ gender self‐presentation on pages of VKontakte. The author points out the importance of students’ visual self‐presentation and their own gender preferences in the design of profiles on VKontakte. Another study carried out by Shchekoturov (2012a) found the features in social virtual gender identity of students. Despite a variety of multimedia functions on VKontakte, students use in the main only avatars and subscribers on public pages and online communities. Additionally, Baklanova (2011) identified some special features of using social media marketing for the promotion of higher education institutions. The author has classified the target groups for an effective advertising campaign of the educational institution. The author also considered VKontakte as the most successful example of social media marketing in education. The question of using social networking in education in Kazakhstan still remains little explored. In our research work we tried to find examples of the use of social networks in Kazakhstani scientific literature, but unfortunately today, such studies are just beginning to develop. But given the fact that the education system in Russia and Kazakhstan are very similar (as in other countries of region), we can state that the Russian experience in using the social network VKontakte is very close to that of Kazakhstan. In order to partially fill the gap in the use of social networks in higher education in Kazakhstan we conducted our experiment and student survey.

2. Methodology The Philosophy Department of S.Seifullin Kazakh Agro Technical University offered a Sociology course leading to the B.A. degree in the winter semester of the 2012‐2013 academic year. This course contributed to marks for the students’ degrees and had a duration of 15 weeks (from September to December). The course provides the introduction to the main sociological theories and methods. This course was compulsory for all first and second‐year students. In early November 2012, we created an educational group on the social network VKontakte ‘Sociology (Astana)’. After completion of the course, we conducted the student survey about the use of ‘Sociology (Astana)’ for studying the Sociology course.

2.1 Student survey We asked students to evaluate how they had used the social network VKontakte in studying the Sociology course. The main purpose of our survey was to identify how students use the social network VKontakte for developing effective relationships in the classroom and how they use mobile devices for access to educational materials. For this purpose we prepared a paper‐based questionnaire. The questionnaire includes three closed‐ended questions and seven open‐ended questions. The student survey was conducted in an anonymous form.

2.2 Participants The participants of the survey were students (n=100, 34% female) of S.Seifullin Kazakh Agro Technical University in Astana. The students were from two age groups (92% under 21 and 8% from 22 to 31). The students were from six different specialties (Agricultural Engineering and Technology, Architecture, Electric Utilities, Agronomy, Combined heat and power, Plant protection and quarantine). The survey was conducted in December 2012 (winter semester).

441

Daniyar Sapargaliyev and Assel Jetmekova

2.3 Course structure The course consisted of five main topics for seminars, which focused on the theme of conflicts in society. These topics were discussed by the students in seminars, as well as in the educational VKontakte group 'Sociology (Astana)'. In this VKontakte group, the students had to comment on and reflect their own opinions about the topics. In addition each student had to describe one example of social conflict and one example of deviant behaviour in Kazakhstani society (for example by adding web links to articles, TV or radio reports). All completed tasks were evaluated by the teacher and other students). The lecturer provided four topics for discussion only for VKontakte group 'Sociology (Astana)' during the semester. These topics were actively discussed by the students. Learners wrote comments and shared web links with each other. One of the most important tasks was the creation of new topics for discussion. During the course students were able to add different types of media, such as video files, photos, PDFs and web links.

3. Results We have processed and analyzed all the data obtained in our survey. Students responded to our questionnaire after the lecture on average for 20‐25 minutes. In general, students did not have any trouble in answering the questions of the questionnaire. In this section, we show the question wording of the questionnaire and the survey results. Q1. How long have you been using VKontakte? Survey data show that all students (100%) are using VKontakte in different time periods. Four in ten say (38%) that they have been using this social network for more than a couple of year. As can be seen from Figure 1, a quarter (26%) of students have experience of using VKontakte for up to two years. The survey also shows that many students have been using a social network for less than one year (14% and 22% respectively) The data demonstrate that the majority of students have long experience in using VKontakte.

Figure 1: Periods of use of VKontakte by students Q2. How often do you use VKontakte? In our survey, it was important to know the frequency of use of VKontakte in the daily life of a student. We tried to find out how often students update the VKontakte features that can be found on the social network website, for example, messages, notifications, requests of friends and groups. Survey data show that three fifths (57%) use the social network several times a day. At the same time 14% of students update VKontakte only once a day. And one in five (21%) student uses VKontakte only several times a week. A small proportion (4%) rarely use social networks in everyday life (Figure 2).

Figure 2: Frequency of use of VKontakte

442

Daniyar Sapargaliyev and Assel Jetmekova Q3. How do you usually access VKontakte? In our survey we tried to identify what type of equipment the students use for access to VKontakte. We think that it is important to know how students use different devices to access a social network. The survey data show that students have identified two main types of equipment (PCs and mobile devices). Obviously, almost all students (93%) use a personal computer for access to a social network. Students also say that they use other types of PC: 5% use a computer in the classroom, 4% in public places and 3 % in the workplace. However, 36% have access to VKontakte from their mobile devices. We think that in the near future most of students will use mobiles on a social network (Figure 3).

Figure 3: Types of equipment for accessing VKontakte Q4. Have you ever used your mobile phone/device to access VKontakte? In our study, we tried to determine the students’ experience in using mobiles to access the social network. Obviously, the popularity of mobile devices is increasing especially amongst the young generation. Today many social networking services have a mobile version. The findings of our study show that three quarters (78%) of students use mobile devices to access VKontakte; at the same time, 22% percent do not have access to social networking via mobiles (Table 1). Table 1: The use mobiles for access to VKontakte group ‘Sociology (Astana)’ Question Q4. Have you ever used your mobile phone/device to access VKontakte?

Yes 78%

No 22%

Q5. Have you ever used VKontakte to learn something?

70%

30%

Q6. Have you ever used your mobile phone for access to VKontakte group ‘Sociology (Astana)’?

65%

35%

Q5. Have you ever used VKontakte to learn something? The data show that most students (70%) have experience in learning through the social network. However, another part of the students (30%) have not used VKontakte for educational purposes. It would be of interest to know that all students who participated in our VKontakte group studied the learning materials. At the same time, in this question, we asked students about their previous experience of using VKontakte for learning (Table 1). Q6. Have you ever used your mobile phone for access to VKontakte group ‘Sociology (Astana)’? In this question we attempted to determine whether our students use mobile devices to access the course materials that have been placed on VKontakte. Perhaps it was more convenient for students to comment on the tasks and topics of the course. Nevertheless only a third (35%) used mobiles for access to VKontakte group and 65% did not use smartphones on ‘Sociology (Astana)’ (Table 1). Q7. How active were you in the VKontakte group ‘Sociology (Astana)’? In our survey we tried to determine the degree of students’ activity in the educational VKontakte group. Students identified seven degrees of activity. The majority of students showed their active participation in ‘Sociology (Astana)’. For example, 6% demonstrated being extremely and strongly active (5% and 1% respectively). A third (28%) mentioned being moderately active, meanwhile one in five (20%) identified “active” degree. At the same time, other students identified weak activity of participation. For example, a

443

Daniyar Sapargaliyev and Assel Jetmekova quarter (25%) of the students said “somewhat active”, and other learners rated their participation as “slightly active” and “not at all active” (14% and 8% respectively) (Figure 7).

Figure 7: Activity of students in VKontakte educational group Q8. To what extent do you agree or disagree with: “I develop more effective relationships with other students in the class by using VKontakte”? The findings of our study demonstrate that the majority of learners developed their relationship more effectively with other students. A third (33%) said “strongly agree”, 18% mentioned “mostly agree” and 8% of the students “slightly agree” with this statement. At the same time, 34% identified a neutral level of development of relationships. However, only a minority of students did not agree with this statement. For example, 3% indicated “slightly disagree” and 4% strongly disagreed with the fact that participation in the VKontakte group helped to develop effective relationships with other students (Figure 8).

Figure 8: Development of effective relationships by VKontakte Q9. To what extent do you agree or disagree with: “The use VKontakte educational group helps me to extend my knowledge in sociology”? The vast majority of students agreed with this statement. For example, 12% mentioned “strongly agree”, one in five (21%) indicated “mostly agree” and almost half the students said “slightly agree” with the statement. Moreover 15% showed a neutral evaluation about the extension of knowledge by the use of VKontakte. And only 5% said “mostly disagree” and 2% indicated “strongly disagree” with the statement.

Figure 9: Improving knowledge in sociology by VKontakte Q10. What would you like to share about your experience in use VKontakte group ‘Sociology (Astana)’? At the end of our survey, we asked students to comment on their experiences in using VKontakte for learning. We received many responses and evaluations from our students. We divided responses and comments into three groups: negative, neutral and positive (Table 2).

444

Daniyar Sapargaliyev and Assel Jetmekova Table 2: Students’ opinions about using VKontakte for learning Evaluation Negative

Neutral

Positive

Student comments "…Most of the group participants were not aware of the dispute and it was impossible to debate with them. They basically copied the information from the Internet without having any opinions. It was not interesting for me." "…Not bad, but most of the participants commented the opinions of others, rather than themselves." "…Everything's great, but most of the participants posted their opinions in order to get a high score. In my view, we should exclude bias. (How? I do not know)." "…I think the idea (to use VKontakte) had no time for proper development. And I cannot judge about benefits or futility." "…I think that the creation of such groups as Sociology (Astana) helps us develop our thinking and allows being involving in the discussions." "…I liked the group Sociology (Astana), I think that this method should be introduced in other courses." "…It was quite unusual, because in the past I did not use social networks to study. I think this experience helps to know more about opinions of other students."

Finally we have a balanced overall assessment of the capabilities and limitations of the use of VKontakte for learning activity. It is important to note that all the students had first experience of using VKontakte for learning.

4. Discussion During the seven years of existence VKontakte has rapidly increased popularity especially among the young generation. This is primarily due to the fact that the interface of VKontakte originally existed in the Russian language. Kazakhstan can be characterized as a bilingual country. The most common languages are Kazakh and Russian. Today VKontakte has interface in three languages (Russian, Ukrainian and English). The majority of our students have had daily access to the social network VKontakte. All the students of our VKontakte group had sufficient skills to work with the social network. It is important to note that registration on VKontakte is allowed from 13 years. Perhaps many of the students had been registered on VKontakte before entering the university. Most Kazakhstani citizens over 35 use the social network and email service of another popular Russian web site Mail.Ru. We have chosen to create our VKontakte group (for students under 21) taking into account the age preferences of users in Astana. Other social networks, especially international ones, have not so popular in Astana. For example, Facebook is constantly gaining popularity in Kazakhstan, but we should know one important aspect. Facebook does not have a large number of users in Kazakhstan, in contrast to VKontakte, because VKontakte is a regional social network which unites the former Soviet Union countries (Kazakhstan, Russia, Belarus, Ukraine and others). Facebook is a global social network, but the level of internationalization of the Kazakh students is still not high enough. We think that VKontakte will remain one of the most popular social networks in Kazakhstan for a long time. Despite the fact that most students have traditionally used a personal computer to access the social network VKontakte, quite a large number of students are increasingly using mobile devices (including smart phones) to access the Internet. It should be noted that in Kazakhstan mobile internet traffic is still expensive for students. But for last two or three years Kazakhstani telecommunication companies have been offering mobile versions of social networks with no‐cost connectivity (Facebook and VKontakte). Students are increasingly using mobile devices to access VKontakte. However, we found that only a third of the students had access to the Sociology (Astana) group by the use of mobiles. We asked our students to add new comments and to read news feed of the VKontakte group on their mobile devices. Moreover we have resolved to use mobile phones in the classroom. The main purpose of this decision was to show the students the opportunity to participate in a virtual learning group at any time. As it was shown by our survey, students agreed that participation in the educational group VKontakte had helped to develop their knowledge on Sociology. They noted that their work in the educational group helped to add to the learning experience. Students liked the unusual approach to studying the Sociology course.

445

Daniyar Sapargaliyev and Assel Jetmekova Perhaps our informal study of Sociology had attracted interest from the students. It is important to note that the students created their own topics for seminars and discussed different sociological themes. We tried to show the students the opportunity to use VKontakte for educational purposes. We explained to the students that social media can be a tool that can be successfully used for creating their own learning content. The students created their own videos, Power Point presentations and posted the files on the Sociology (Astana) group. However, there were some critical and negative opinions about organisation in our educational group. Another important aspect of the use of the Sociology (Astana) group was that the majority of students mentioned the effective development of relationships among members of the educational group. It should be noted that some students who communicated on VKontakte never talked face to face. But they were able to meet each other and to develop communication skills through participation in the Sociology (Astana) group. In general we can say that our study has shown that we do not disclose the full potential of the use of social networks in the learning process of the university. This is due to the fact that we had some limitations. All students have their VKontakte profile, but many of them do have not permanent access to the Internet. This problem we have partially solved by increasing the use of mobile devices by students with free access to the network VKontakte. However, we need to continue to pursue in‐depth research into the use of social networks and mobile devices in teaching and learning.

References Alexandova, A.Y. (2012) “The use of social networks in learning process. Theory and history of state and law”, [online], http://lib.grsu.by/library/data/resources/catalog/172171‐388307.pdf#page=131. Babin, E.N. and Redko, N.V. (2011) “Social networks as a web service in the organization of the educational process in higher education”, [online], http://it2011.petrsu.ru/thesis/16.doc. Baklanova, E.M. (2011) “Effective marketing of educational services in social networks”, [online], http://vernadsky.tstu.ru/pdf/2011/03/15.pdf. Bikunov, A.S. (2012) “Experience a combination of distance learning platform SAKAI and social network VKontakte for support learning process”, [online], http://ict.informika.ru/vconf/files/10300.pdf. Feshenko, A.V. (2011) “The use of virtual social networks in the educational process of the university”, Institute of Distance Education of Tomsk State University, [online], http://ido.tsu.ru/files/pub2010/Feschenko_Ispolzovanie_virtualnyh_ socialnyh_setei1_.pdf. Gerkushenko, G.G. and Sokolova, S.V. (2012) “Prospects for the use of social networks in the practice of education” [online], http://iktgio.mcrt.ru/rus/info.php?id=9726. Kovalenko, A.V. (2011) “Social Networks & Information Support of teaching practices (An example of social network VKontakte)”, [online], http://93.190.41.93/alyona_kovalenko_soc_nets_for_teacher2011.doc. Kribel, S.S. and Shobukhova, V.V. (2012) “The use of social networking in education”, Computer Science and Education, Vol 1, No.4, pp 66‐68. Libshner, A. (2010) “We will stay in VKontakte ‐ communication in social societies of Russian Internet”, In the world of scientific discovery, Vol 2, No.1, pp 142‐143. Morgunova, O.V. (2011) “Technology Web 2.0 of libraries in social networks”, [online], http://dspace.susu.ac.ru/xmlui/bitstream/handle/0001.74/1172/1.pdf?sequence=1. Olshevskaya, A.V., Pilenko, D.N., Silich, I.V., Kuznetsova, I.V., Shtennikov, D.G. and Nikolayev, D.G. (2011) “Use of opportunities of social networks to organize the process of distance learning”, [online], http://www.ict.edu.ru/vconf/files/10020.pdf. Shchekoturov, A.V. (2012) “Web 2.0: Features, strategies, and technologies of gender socialization of teenagers on Internet”, Bulletin of the Nizhny Novgorod University, Vol 4, No.1, pp 437‐444. Shchekoturov, A.V. (2012a) “Constructing virtual gender identity in web pages of teenagers on social network VKontakte ”, Women in Russian Society, Vol 1, No.4, pp 31‐43. Shipicin, A.I. (2011) “The phenomenon of social networking in modern culture”, Proceedings of the Volgograd State Pedagogical University, Vol 57, No.3, pp 36‐40. Wikipedia. (2013) “VKontakte”, http://ru.wikipedia.org/wiki/VKontakte. Zakurdayev, A.A. and Lavrov, A.V. (2012) “The introduction of a learning management system in the social network”, [online], http://www.ict.edu.ru/vconf/files/12238.pdf. Zhukova, N.S. (2011) “Protection of information in social networks in the educational process”, [online], http://www.gosbook.ru/system/files/documents/2011/12/01/works_ito‐xxi.pdf#page=468.

446

Automatic Creation of Semantic Network of Concepts in Adaptive e‐ Learning Emilie Šeptáková The University of Ostrava, Ostrava, Czech Republic eseptakova@gmail.com Abstract: This paper deals with the design and utilization of a semantic network of terms in adaptive e‐learning. It states what is meant by the adaptive e‐learning, describes the general structure (metadata) of adaptive e‐learning material and how the proposed structure of the semantic network of terms is connected. The paper defines a coloured semantic network as a directed weighted graph with vertices and edges. The vertices represent different types of concepts and the edges represent relationships between vertices. It also defines the functions and methods suitable for working with this network. The paper discusses the possible types of concepts appropriate for e‐learning and the relationships between them and the possibilities for automation of the semantic network creation. A user‐intelligible way of the semantic network visualization in the form of a graph with suitably chosen vertex types and edges is also designed to distinguish the semantics of concepts and relationships graphically and clearly. Further, the paper describes the potential use of the semantic network by a student during his/her study and by the teacher – author during the development of learning materials. Students may use the network for better understanding of concepts, their definitions and their subsequent use to be followed by better memory of individual links between concepts. They should also gain better orientation in the entire study issue. The visualized semantic network allows students to go through the learning material in a different way, contrary to the traditional sequential way. Also teachers may benefit from visualization of the semantic network by displaying the relationships between important concepts. In that, the teacher‐author may check the text and correct the sequence of terms, providing the possibility to optimize or correct the text to meet the pedagogical principles of creating high‐quality learning material. The algorithm of the semantic network creation uses the principle of adaptive e‐learning, e.g. dividing the educational text into small homogeneous parts showing their meaning in the metadata. The important concepts or terms selected by the teacher‐author are placed in the semantic network and then labelled in the place of their definition in the theoretical part of the learning text. All occurrences of these selected terms are automatically detected in the rest of the learning text and placed into the semantic network as well, including information where these terms are located. Furthermore, among these concepts various other types of relationships are automatically identified. In conclusion, the paper indicates the method of putting automatic network creation into effect, where data structure with a direct link to the metadata of adaptive learning material is designed followed by the basic methods for finding and identifying types of concepts and their relationships. Keywords: semantic network of concepts, adaptive e‐learning, learning material, metadata, visualization

1. Introduction At present, the part of instruction in the form of e‐learning courses is constantly increasing. The students in the combined form of study, but not only them, are supposed to be able to learn individually from suitably structured study materials. In the case of adaptive teaching, this learning process should be more efficient, because the subject matter is presented to the student in the form which suits his/her learning style. Mostly, the subject matter content has a linear form. The important concepts or ideas are highlighted graphically in written form (underline, italics, semi‐bold type), by means of intonation in speech. Thus, the student is given certain signals that something is important. The student’s task while learning is not only to remember some information, but also relationships concerning it. He must find them in the text and construct the structure of the subject matter. S/he also looks for new relationships to the things s/he already knows and incorporates them into his/her existing knowledge structure, or s/he reorganizes this structure. These skills – to structure written or spoken text – are not purposefully taught and the students often acquire them by trial and error method, or they create incomplete or incorrect structures. Sometimes, also a textbook or, more often, a teacher’s interpretation contains inappropriate, incomplete or badly organized structure, which makes it harder for the student to learn. In the easiest case, this subject matter structure has a form of a tree or a more common form of a network, i.e. it is not linear. In the 1980s, research showed that there are two types of memory – an episodic and semantic one. The episodic memory stores information on events taking place in a certain context, at a certain time according to their space‐time coordinates. The semantic memory receives information on words and other verbal symbols, their meanings and relationships, on rules and algorithms for working with symbols, concepts and relations.

447

Emilie Šeptáková For structuring the subject matter and visualization of this structure, it will be useful to apply the idea of the semantic memory and the structure of text expressed in the form of a graph using 2 basic concepts: vertices and edges which enable to construct richly structured networks, (Čáp 2007 chapter 14). My task is to find and describe the structure of the author’s texts, automatically creating and subsequently visualizing the concept network so that the student may use it to remember concepts and relations among them better, and so that s/he may go through the study text also in nonlinear way.

2. Theoretical background The subject of the article concerns several scientific fields. In the following section I will briefly summarize present information on adaptive e‐learning and semantic networks that I will refer to later.

2.1 Adaptive e‐learning E‐learning refers to the use of electronic media (video recordings, audio recordings, multimedia presentations, slide presentations and online content) and information and communication technologies (ICT) in education. Adaptive learning can be divided to the following categories: adaptive interaction, adaptation of content, content discovery and assembly, and adaptive collaboration support (Paramythis 2003). Adaptive of Content, changes the structure and presentation of the course in a way, that suites user’s characteristics and optimizes quality and time of learning. This way of adaptation involves dynamical changes in the navigation elements of the course and its structure and dynamical selection of its suitable parts. Speaking of adaptive learning I mean the adaptation of content. The results of research in adaptive e‐learning (Kostolányová 2011) are student’s characteristics, which affect the learning process, initial test for their detection, the rules, how to teach given type of student, and ultimately what must be learning material that could be adapted. In 0 is shown simplified structure of the author’s part for creating learning materials and relationships to new part – the semantic network of concepts (white rectangles) – see chapter 4

Figure 1: Simplified structure of the author’s part for creating learning materials. New part – the semantic network of concepts – and relationships to the author’s part entity types 2.1.1 The teacher’s work in adaptive e‐learning while creating the author’s text The teacher segments his/her text into small parts – components. For each component it is necessary, among other things, to determine which layer it belongs to, i.e. in which stage of the teaching process this component will be used. The possible types of layers are – theoretical (TL), semantic (SL), fixation (FL), resolved examples (SE), practical (PR),questions (QL), tasks( TA), practical exercises (PT), goals (GL), motivational (ML), navigational (NL), literature (LL), and non‐adaptive, for non‐structured part of the learning material. Concepts are supposed to be defined in the theoretical layer and subsequently used in all the other layers.

448

Emilie Šeptáková The author matches each layer with a sensory variant (the student’s preferred learning style – verbal, visual, kinaesthetic, audible) and the depth of the presentation. S/he will place the variants in the framework. Within one framework, the variants concern the same subject matter, but they differ as far as the way of presentation and verification is concerned. S/he inserts the frameworks containing a unit piece of information in the lessons. The subject matter in one lesson corresponds to one class lesson. Then, the lessons can be inserted in the subject chapters. Concepts are supposed to be defined in the theoretical layer and subsequently used in all the other layers. The teaching system, so called virtual teacher, gradually presents the frameworks to the student in the variant corresponding to his/her learning style. Another way how to create the possibility to go through the linear text nonlinearly is to supplement the text with hypertext references enabling the student to find the relevant information written elsewhere faster. However, these references must be created manually by the author and the way s/he describes the text structure as well as the number of references depend on his/her experience and the time s/he devotes to this activity. If there was an automatically formed semantic network of concepts (see 2.2) in adaptive e‐learning, the teacher, while creating the learning material, will only mark the defined concept, which will be inserted in the semantic network of concepts and this network is subsequently supplemented with all the other occurrences and addresses of this component in all the other components of the subject, as well as with the relationships among these concepts. Graphic visualization of such network would enable the teacher to check the learning material structure in the relationship to the defined and automatically found concepts, and to upgrade the author’s text according to their potential nonexistence in the individual layers. 2.1.2 The student's study in adaptive e‐learning While doing a particular course, the student usually goes through the individual lessons sequentially as they are presented by the virtual teacher in the most suitable form for him/her, s/he learn new information, new concepts from the definitions, verifies them using examples, finds mutual connections, tests his/her knowledge by means of auto tests, etc. Another possibility is to go through the subject matter according to hypertext references created by the teacher‐author to enable the student to find the relevant information written elsewhere faster. A new way how to go through these learning materials and to consolidate, learn concepts and realize relationships among them, is orientation according to the "concept network". Both the student and the teacher could go through the learning text according to the concepts and relationships among them – when the student/teacher can be interested where the same concept is used, in which context, where the other highlighted and important concepts are used in the given definition, or what other concepts s/he needs to know for defining the given concept, where their definitions are or what other concepts s/he has to study to understand a certain concept on the basis of another one. The learning process itself in the environment of adaptive e‐learning using the virtual teacher should be more efficient than the process without the possibility of adaptive learning. At present, LMS do not enable the teacher to highlight the defined or important concepts necessary for understanding the subject matter in the relevant lesson. It is not possible to automatically find these concepts in the whole text of the course, and to automatically create a network of concepts which would enable to show the concepts and relationships among them within the framework of the lesson or the whole course. However, such a network of concepts would be very useful for the study. It implies the need of my intention – defining the data structure and methods for the automatic creation and utilization of the semantic network of concepts in e‐learning with regard to adaptive teaching.

449

Emilie Šeptáková

2.2 The semantic network of concepts and its visualization One possibility how to record concepts and relationships among them is the semantic network of concepts. Definition from (Lukasová 2010) states: Associative network (semantic network, hereinafter SN) is an evaluated graph consisting of vertices, evaluated by terms, and edges, evaluated by binary predicate symbols, while the edges connect some vertices pairs. Concepts (terms) are words or collocations that have their meaning in the given context. These meanings can differ from the meaning of the same words in a different context or in everyday use. Terminology as a discipline, among others, studies how such terms emerge, and their relations within the given culture. In contrast to lexicography, which studies words and their meanings, terminology studies concepts, conceptual systems and their terms. SNs were first invented for the computers by Richard H. Richens (Richens 1956) in the language research department in Cambridge in 1956 for the machine translation of natural languages by means of computers. SN was first implemented by Robert F. Simmons (Simmons 1963), M. Ross Quillian (Quillian 1963) and Allan M. Collins (Collins 1969) in the 1960s for modelling the semantics of English sentences. J. F. Sowa (Sowa 1987) divides semantic networks more precisely into several categories according to the veracity of assertion, types of relationships, what they represent – definition, assertive, implication, executing (starting), learning and hybrid. In the graphic form, the associative networks are a simple and easily understandable means of formal representation of knowledge for clarification of the meaning of words in a wider context. An atom of the semantic network is represented by an assertion (statement) which has the form of a vector – subject – it has a property – object, or (〈subject〉, 〈predicate〉, 〈object〉) (e.g. the student studies a subject).The atom of the network can be graphically represented as two evaluated/named vertices conned by an edge of the property (predicate). subject

predicate

object

Figure 2: Graph representation of an atom of the semantic network The individual node types can be value from the attribute domain (“Joseph Smith”), concept – set of entities (constant ‐ dog, tree)or variable (X), entity (instance) ‐ constant (small initial letter –mickeymouse) or variable (capital letter ‐ X, Y, Z), name of function – symbol of function – mother (X), sin (Y) and term of existence ‐ @somebody, @famous(X). The edges represent the relationships– ISA, the name of attribute, verb. In 0 you can see SN graph example, where all vertices and edges are drawn as well, even if they are of a different type.

Figure 3: SN graph example

450

Emilie Šeptáková

3. The semantic network of concepts in adaptive e‐learning A concept in this case is a defined word or collocation contained in the learning material. All the records of the information on the concepts will be kept in the database including various metadata, e.g. an address identifying the place where the concept is located. An automatically found identical concept also has the address where it occurs. A relationship is the interaction between 2 different concepts, metarelationship is our term for the interaction between the same concept both the defined and the automatically found one. 3.1.1 The proposal of SN My task is to automatically construct the semantic network of concepts for adaptive learning materials. To do that, it is necessary to define the structure of individual entities, then to recognize the concepts as the main vertices and to recognize the relationships among the network vertices. Types of Vertices in SN I will distinguish several types of vertices in the semantic network. One type of vertices will contain the defined concept and another type of vertices will contain an automatically found concept which will either be automatically found in the theoretical layer without the defined concept(occurrence before or after the definition) or automatically found in some other layer, not the theoretical one (occurrence before or after the definition and the layer type).The predecessor of the given concept is the concept, the type of vertex which occurs in the definition of the given concept. The successor of the given concept is the concept which uses the given concept in its definition. Types of Relationships among Concepts in SN Thus, this use of the SN displays mainly other relationships than the traditional network. It displays the structure of the author’s text and the relationships among the concepts in this text. This type of network has two basic meanings: for the author, for debugging and optimization of the learning material and for the student, for better orientation in the text and creating a structure in his/her memory faster. In the semantic network, various kinds of relationships can be defined, those ones that I will use are stated below, isa hierarchy, isPredecessor, isSuccessor and metarelationships occurrenceBefore – occurrence of this concept before the definition of this concept, occurrenceAfter – occurrence of this concept after the definition of this concept. One of the possible relationships in the semantic network in adaptive e‐learning is a relationship called isSuccessor – when the defined concept is also automatically found in the definitions of other concepts – these are Successors of the selected defined concept, and I call the relationship between them and the defined concept isSuccessor. Similarly, the relationship isPredecessor – when for the definition of the concept other (earlier defined) concepts are needed – Predecessor – and between the defined concept and Predecessor is the relationship isPredecessor. The relationship – occurrenceBefore the definition – is a metarelationship when the same concept occurs in the author’s text before its definition. With regard to the type of layer goals or motivations it is all right and it probably suggests that the concept is important; therefore it is mentioned before the definition. However, if e.g. an automatically found concept is used in a task and it has not been defined yet, it is the case of using a concept before the definition and the author should consider if it is all right, or place the task after the concept definition.

451

Emilie Šeptáková The relationship occurrenceAfter the definition means that the concept is properly first defined and then used either in the theoretical layer without the definition of another concept or in another layer. It is suitable to use the concept in more layers (motivational, semantic, resolved examples, tasks, etc.) so that could be consolidated well. Herein, it will be interesting to determine in which layer the concept is not used – the author should ask himself/herself if the given layer should be created using the given concept.

Figure 4: Relationships and Metarelationships between the concepts in sn in e‐learning This SN displays not only relationships from the reality described in the text, but also metarelationships depicting the occurrence of concepts in the text, therefore we will call it the occurrence one. I will match other metadata to the defined concept

the address of the place where the defined concept is located,

the addresses of automatically found occurrences of this concept of each type,

the number of automatically found identical concepts,

the number of direct predecessors,

the number of direct successors,

the number of occurrences of the concept before the definition,

the number of occurrences of the concept after the definition,

the number of occurrences of the concept in individual layers (theoretical, semantic, motivation,…).

This all can relate to

the selected layer,

the selected framework,

the lesson,

the subject,

all the subjects.

Another type of the relationship occurring in semantic networks is the relationship of the hierarchy, where we distinguish the superordinate, subordinate concept and this relationship is termed as ISA. If the vertex has only one superordinate vertex, the ISA relationships form a tree (i.e. hierarchy)where the most general concepts, so called categories, are at the top. It will not be possible to create this type of relationship automatically, but the teacher can find the superordinate concept while defining the concept. If the vertex will have more superordinate vertices, we can speak of a general network, and it will be necessary to define a new structure of entity type for the superordinate‐subordinate relationship. 3.1.2 The teacher’s work in adaptive e‐learning – definitions of concepts in SN The way the teacher creates his/her text is described 2.1.1. In the case of automatically formed SN in adaptive e‐learning, the teacher will be offered the possibility to highlight the defined concept while writing the text in the component; the highlighted concept will be inserted in the semantic network of concepts and this network

452

Emilie Šeptáková will be further automatically supplemented with the address where the definition of this concept occurs and with all the other occurrences and their addresses in all the other components of the subject as well as with other metadata concerning the defined concept and relationships and metarelationships among these concepts and the concepts defined earlier. Graphic visualization of such network would enable the teacher to check the learning material structure in relation to the defined and automatically found concepts, and to upgrade the author’s text according to their potential nonexistence in the individual layers. 3.1.3 How the student benefits from the semantic network Using the semantic network while going through the subject matter will enable the student to understand concepts and relationships among them better, it will also enable him/her to visualize the structure of the study material − s/he will remember the concepts and relationships among them better. For example, while reading a definition, the student can see the highlighted word or collocation in the text, when the corresponding part of the network is displayed, s/he can see the related concepts, those ones that have already been defined (and s/he should already know them) − so called Predecessor, and also those ones that will be defined – so called Successors, including the references to the places with definitions of these concepts. By means of the semantic network, the student will be able to move in the learning materials not only sequentially, but s/he will also see the relationships among the studied concepts. 3.1.4 The proposal of a multicoloured semantic network In the SN (defined in the chapter 2.2) various types of vertices and edges are plotted in the same colour and shape. It is not possible to distinguish the vertices type by merely looking at the SN graph. If we determined different colour for the individual vertex types, the multicoloured semantic network should become more clearly arranged. If we also highlight the meaning of the individual vertex types represented by different colours by different shapes of the individual types of entities, SN can become even more transparent and more clearly arranged.

Figure 5: Multicoloured SN from 0 including the different shape of vertices types It is also necessary to consider if using different types of edges as well is of any use – the colour, and possibly the thickness and type of the line, or various size of vertices – according to the size of an attribute.

3.2 Visualization of the semantic network and a suitable software At the beginning, the easiest solution can be listing of information on vertices and edges in a table, if we choose a small part of the network – the predecessors and successors of the selected concept, even a simple table can show information value.

453

Emilie Šeptáková The semantic network is represented by a graph, therefore it is beneficial to determine the graph properties as well: if it is regular, continuous, the number of vertices, the number of edges, planarity, and if these properties could be used for the graph visualization. Other algorithms will probably be necessary – searching for the shortest way, searching in width, in depth, and others. It will also be necessary to solve in this part:

In general, while solving the diagram visualization, a non‐trivial problem arises – the spatial arrangement of vertices to make the diagram clear, transparent and well‐arranged,

to try to find a programme (e.g. Gephi), or libraries of the programmes that can draw a diagram and are suitable for adaptive e‐learning system in the Internet environment (yEd, Graphviz),

to find and select suitable arrangement of the diagram (hierarchical, tree‐like, circular, right‐angled, organic, spherical, and others) for its optimum display,

to find a suitable data format for the diagram data transfer into the selected programme, how to write the information on the vertices colour and shape

to implement the functions for the semantic network data export from the database into the selected data format,

to enable the student to go through and study the learning materials using the relationships in this semantic network of concepts.

Solving this problem is not trivial and the issue is deal with by the specialists in the area of graph topology. With wide‐ranging graphs with a large number of vertices, the problem becomes even more complicated – the advantage is dynamic zooming in and zooming out.

4. The practical part So that we can automatically create the semantic network of concepts for adaptive learning materials, it is necessary to define the data structure of this SN of concepts in e‐learning. I have proposed the SN structure consisting (minimum) of two entity types NetD and NetA which are lower described in detail. NetD – contains the defined concepts highlighted by the teacher. NetA – contains the automatically found concepts. Table 1: The attributes of NetD and their short description id_D addressD term explanation feature url address id_DR no_found no_ predecessors no_ successors no_occur_before no_occur_after no_XX

unique identifier the address of the defined term the highlighted term – the concept the author’s notes about the term ‐ optional disable/enabled for future usage id of the superordinate concept – for the hierarchical structure ‐ optional no = number of ... the same terms automatically found in different locations the direct predecessors the direct successors occurrences of the concept before the definition occurrences of the concept after the definition occurrences of the concept in XX layer – where XX in TL, SL, FL, SE, PR, QL,TA, PT, GL, ML, NL, LL, see 2.1.1

Table 2: NetA – The attributes of NetA and their short description id_A addressA id_D feature relationship_type layer_type url address

the unique identifier the address of the automatically found term the definition of this term disable/enabled the type of the relationship with the defined term the type of the layer in which this term is placed for future usage

454

Emilie Šeptáková In general, the semantic network data structure should consist of the above mentioned entity types, and 0 shows linking‐up the proposed SN structure to the existing structure of the author's part of the adaptive e‐ learning system.

4.1 The example of visualization of the part of the SN result Let us have 3 definitions from the area of the database theory(simplified) using the concepts defined earlier for the definition of 3 new concepts. Definition 1NF: If the schema of relation R only contains atomic attributes, we say that it is a normalized relation or that it is in the first normal form (1NF). Definition 2NF: The schema of relation R is in the second normal form (2NF) if and only if it is in the 1NF and each secondary attribute (non‐prime attribute) is fully dependent on each key of the R schema. Definition 3NF: The relation schema R is in the third normal form (3NF) if and only if it is in the 2NF and no secondary attribute is transitively dependent on any key of the R schema. 0 shows the text with three definitions with the highlighted concepts – in red – the defined concepts, in blue – occurrence of the concepts defined earlier somewhere in the previous text, used here for the definition of three new concepts. Above this text there is a network of the defined concepts (red rectangles) with relationships among them (in red, dark) and occurrence of automatically found concepts (light blue rectangles).

Figure 6: Motivation for SN visualization, theoretical layers with definitions and terms in SN 0 already shows part of the semantic network without auxiliary relationships with the depicted relationships with respect to the selected concept 2NF (solid arrows), 3NF (dashed arrows).

5. Conclusion The main objective of my paper is to visualize the structure of the adaptive e‐learning material as feedback for the author of the text and as the support for better orientation in the textbook for the student. The introductory chapter outlines the context related to the topic of the semantic network of concepts in e‐ learning. The first chapter sums up individual terms like adaptive e‐learning, concept, semantic network of terms and how it can be visualized. The third chapter contains the proposal of the structure of the semantic network of concepts, enumerates the advantages of the network visualization for the author and the student. It also describes and presents the drawing of an example of various types of relationships among the concepts within the semantic network of terms. The practical part defines the semantic network data structure and presents the diagram of its incorporation into the existing structure of the author’s part of diagram of adaptive LMS.

455

Emilie Šeptáková

Figure 7: Relationships isPredecessor (P) and isSuccessor (N) among the concepts in part of SN Contrary to ontology and RDF describing the objects of the world and relationships among them, my proposal of the SN variant describes the author’s text, learning material, and relationships within the text, which may not always correspond to the reality. In the future, I am going to implement this proposed network as a module for the rising adaptive LMS at the Pedagogical Faculty in Ostrava, and obviously let the authors of study texts and the students test it.

References Collins, Allan M. and M. Ross Quillian, (1969) Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, Volume 8, Issue 2, April 1969, Pages 240‐247, ISSN 0022‐5371, 10.1016/S0022‐5371(69)80069‐1, http://www.sciencedirect.com/science/article/pii/S0022537169800691 Čáp, J. and Mareš, J. (2007) Psychologie pro učitele. Vyd. 2. Praha: Portál, 655 s. ISBN 978‐807‐3672‐737. Kostolányová, K. Šarmanová, J. and Takács, O. (2011) Classification of learning styles for adaptive education. New educational review. Vol. 23, issue 1, p. 199‐212. ISSN 1732‐6729. Kostolányová, K. & Ostravská univerzita (2012) Teorie adaptivního e‐learningu. Vyd. 1. Ostrava: Ostravská univerzita v Ostravě. ISBN 978‐80‐7464‐014‐8. Lukasová, A., Habiballa, H., Telnarová, Z. and Vajgl, M. (2010) Formální reprezentace znalostí. Vyd. 1. Ostrava: Ostravská univerzita, 345 s. Universum (Ostravská univerzita), 13. ISBN 978‐80‐7368‐900‐1 Paramythis Alexandros and Susanne Loidl‐Reisinger (2003) Adaptive Learning Environments and eLearning Standards, In R. Williams (Ed.), Proceedings of the 2nd European Conference on e‐Learning (ECEL2003), Glasgow, Scotland, 6‐7 November (pp. 369‐379). Academic Conferences International Reading 2003; ISBN: 0‐9544577‐4‐9 (2003) Quillian, R.(1963) A notation for representing conceptual information: An application to semantics and mechanical English para‐phrasing. SP‐1395, System Development Corporation, Santa Monica. Richens, Richard H. (1956) General program for mechanical translation between any two languages via an algebraic interlingua. Cambridge Language Research Unit. Mechanical Translation, November 1956; p. 37. Simmons, Robert F. (1963) Synthetic language behavior. Data Processing Management 5 (12): 11‐18. Sowa, John F.(1992) Semantic networks. In: Encyclopedia of Artificial Intelligence, edited by S. C. Shapiro, Wiley, New York, 1987; revised and extended for the second edition, http://www.jfsowa.com/pubs/semnet.htm.

456

Gathering the Voices: Disseminating the Message of the Holocaust for the Digital Generation Angela Shapiro1, Brian McDonald1 and Aidan Johnston2 1 Glasgow Caledonian University, Glasgow, UK 2 University of Strathclyde, UK a.shapiro@gcu.ac.uk Brian.McDonald@gcu.ac.uk aidan.johnston@strath.ac.uk Abstract: This paper outlines the journey that has been taken in developing the “Gathering the Voices Project”. We first discuss the rationale for the Project, the partners we are working with and the expected outcomes. The project is using informal learning approaches to engage with the general population. Glasgow Caledonian University (GCU) is working in partnership with the Gathering the Voices Association (a voluntary group) with the purpose of collecting and digitising oral testimonies of Holocaust survivors who came to live in Scotland. We have completed the pilot phase of the Project and are now in the second phase which is collecting and digitising the testimonies and investigating new innovative ways to deliver the objectives of the project. We want to record their description of their lives and that of their families in Nazi dominated Europe and, especially, their experiences once they gained sanctuary in Scotland for educational and public engagement purposes. Throughout our project, we are seeking the participation of the community in the creation of our educational and public engagement outputs. Our key outputs are involving students in the creation and development of multimedia learning approaches for schools and a travelling exhibition that will take our materials out into the community, as well as providing resources for future educational and Holocaust related events. We have also been fortunate in receiving funding from the Heritage Lottery Fund, Sense Over Sectarianism (Scottish Government), the Federal Republic of Germany, and local community trusts in Scotland. The anticipated learning outcomes use a variety of pedagogical and blended learning approaches, including: collecting and digitising the oral testimonies of Holocaust survivors in Scotland; creating an informative and easy to navigate website including extracts from interviews and photographs of personal artefacts, and hosting a Game/App Jam at GCU. The strategic overall aim of this project is to ensure that resources developed during the project are widely disseminated to a broadly diverse user base. We also intend for the resources to be available for future National Holocaust Day events in Scotland. The British Library has also confirmed that they including our website as part of their Curators' Choice UK Archive. Keywords: holocaust, experience centered narratives, multimedia applications, open education resources (OER)

1. Introduction to the gathering the voices project The Gathering the Voices Association (GtV) is focusing on collecting and digitising oral history of Holocaust Survivors in Scotland. We intend to develop resource materials for formal education, specifically designed for children of school age, including pupils studying at the elementary stage of schooling (5‐12) and pupils attending secondary education (12‐ 18). This will be in collaboration with Sense Over Sectarianism (SOS). The aim of SOS is to teach children about tolerance and understanding among children of different faiths and ethnicities (SOS, 2013). The Gathering the Voices project has as its basis, the gathering of oral testimony of men and women who came to Scotland in the 1930s and 40s as Holocaust refugees. We are interested in recording both their description of their lives and that of their families in Nazi dominated Europe and their experiences once they gained sanctuary in Scotland. This period, when refugees left the European mainland and arrived in Scotland as asylum seekers, has received little attention. Even less documented is their gradual integration into Scottish society and their contributions to the social, cultural and economic life of the country. This, we believe, is an essential part of our heritage. The Association is comprised in total of six volunteers from the Glasgow Jewish Community; all are volunteers with the Glasgow Association of Jewish Refugees (AJR, 2013). ‘The organisation was founded as a Friendly Society in 1941 by Jewish refugees from Central Europe and now has extensive experience attending to the needs of Holocaust refugees and survivors who came to this country before, during and after the Second World War.’ Three of the members of the Association are second‐generation refugees. We have further discovered that though some Holocaust survivors have been interviewed, little had taken place to investigate their lives after the war years. Additionally it was soon evident that it was impossible to

457

Angela Shapiro, Brian McDonald and Aidan Johnston access detailed Holocaust testimonies online without payment. We felt strongly that no one should ‘profit’ from these interviews and that they should be freely available and the best way of doing this was to make them easily accessible online. Detailed interviews have been collated sporadically in Scotland, but all have focused on the journey rather than including their lives in Scotland after immigration. Moreover, none of the interviews we found have considered the Scottish experience in detail. Our research also revealed that although there were interviews online, often there was a charge to listen to them or some interviews only made available sections. When complete interviews are on line they tended to be an individual talking to the camera sometimes for one hour or more, which was difficult in respect of engaging the listener (The Voice/Vision Holocaust Survivor Oral History Archive, The University of Michigan‐Dearborn, 2013). Additionally, many of the interviewees we know have spoken of their concern that their testimonies, in the past have been inappropriately edited and that former interviews had given little attention to their life once settled in Scotland. In response to these comments, we felt strongly that this is a significant part of our heritage that should be freely accessible to all and not something that people should profit from.

2. Pilot project In 2011, we secured £10,000 from the Scottish Government’s ‘Sense over Sectarianism’ (SOS) fund to deliver a pilot project. The pilot was developed in conjunction with teachers from three secondary schools in Glasgow to ensure that our questions for the interviews matched relevant areas of the national school curriculum. We staged a public event in Glasgow and involved volunteers from the Glasgow Jewish Community where six people agreed to be interviewed. The pilot was then officially launched in 2012 in the Glasgow City Chambers as part of National Holocaust week. Second Phase of Project We are now in the second phase of the Project and have already received substantial recognition in that we have been asked to speak to groups in Glasgow and Edinburgh. Publicity has already been significant in that several news articles have appeared in the Scottish national press, in the Jewish UK wide press and in the Glasgow Caledonian University’s alumni magazine and website. We have also been acknowledged by the Scottish Parliament both in a motion to the House and by being invited to speak there. First and foremost, the pilot reaffirmed our belief that there is a deficit in this area of education. We passionately believe that we need to preserve these human interest stories so that we can educate future generations on the importance of the acceptance of other cultures. The testimonies teach that the survivors were ordinary people faced with extraordinary circumstances yet in spite of this they remain positive individuals who went on to make significant contributions to our society. These stories can be related to the challenges facing today’s refugees. The pilot has generated a number of findings that will help to inform how we undertake the full project, for example:

We found it was best to conduct interviews in the interviewees' homes as they were more comfortable and willing to discuss sensitive topics in their own surroundings. In addition, it became complicated asking the interviewees to bring important artefacts to be photographed for the website as many did not wish to have artefacts removed from their home

We gained a true sense of the time and cost of transcribing, editing and digitising the interviews. It very quickly became apparent that this was much more time consuming and costly than anticipated, hence, some of the interviews still require digitisation. The health of some of the interviewees (many are now experiencing poor health, such as emphysema, dementia) made un‐edited versions very difficult to listen to and therefore significant work is required to make them accessible to all.

The survivors reinforced the need for more accurate and detailed accounts of their experiences that truly reflect their stories. Many feel that they have been misquoted in the past or that elements of their stories have been inappropriately edited. The members of the Association have known many of the survivors for over thirty years and they trust us to ensure that their interviews are valid; however, we acknowledge that we need to make sure that we allow sufficient time and resources to address this issue.

Shorter edits were insufficient for learning purposes. The need for exceptionally high quality audio and transcripts for educational purposes was also highlighted by the pilot. This point is further supported by previous research by Shapiro and Johnston (2010).

458

Angela Shapiro, Brian McDonald and Aidan Johnston

3. Experience centered narratives Although participatory action research is useful as it enables this group of elderly and in many instances frail and vulnerable adults to allow their own voice to be heard, and enables them to express their view on their personal histories (Fenge, 2002). The type of interviews adopted the style of the ‘experience centred narrative’ (Squire, 2008 p43).This is when several interviews take place and the participants may also comment on the interviews. Within that process, Winter’s (1996) comments are also of interest in that Winter points out that we need to acknowledge our own perceptions when carrying out the interviews as all of the members of the GtV Association are related or are indeed children of Holocaust survivors. We are taking cognisance of these relationships and the associated variables within our dialogue. An essential component of the ‘conversations’ with the interviewees are that everyone’s contribution is valued and acknowledged. In practice we have ensured that all of the participants or family members have seen the transcripts prior to these being uploaded on to the website. A key component is also recognising that there are plural structures in that some of the interviewees hold different interpretations about their stories even when they have ‘shared experiences’. We have acknowledged different accounts and associated critiques which have led to theory and practice being related and complimentary. As Gubrium and Holstein (2008, p166) observe; ‘ …it is the incorporation of particular items into a coherent account that gives them meaning.’ For many of the survivors, ‘… the stories are no longer conscious elaborations of experiences but the necessary cognitive structures for remembering and making meaning of otherwise lost experience’ (Ekstrom, 2004, p.675). Collecting the oral histories is the key component of this project and through the application of oral histories, we aim to disseminate these rich authentic resources to a whole new generation of learners. Most of the survivors who have recorded oral testimony were of a similar age to many of the school children who will listen to such testimonies, which will makes listening to the interviews more meaningful. We believe it is important that the Holocaust survivors are acknowledged as being major contributors to Scottish society and citizenship, despite having experienced unimaginable horrors. By using and learning from the testimony, we hope that a whole new generation of contemporary young learners will understand more about the challenges facing today's asylum seekers. But we recognise that many of today’s younger learners are confident ‘digital natives’ (Prensky, 2001) and therefore we needed to ensure that the information is disseminated applying suitably, applicable modern, accessible and where possible interactive methodology. We also recognised that the interviews need to be relevant, to that end we have divided the material into themes, ‘before the war’, ‘life during the war’, ‘immigration’, ‘settling in’ and ‘reflection’. Digital storytelling helps links the narratives of the Holocaust survivors to the digital natives but at the same time we need to recognise that mature adults also wish to listen to the stories. We intend to have in excess of 30 interviews on the website. This rich tapestry of individuals’ experiences lends themselves to the digitally diverse platforms. A monograph has a purpose but the spoken voice combined with photographs written words and film extracts make the narrative a powerful experience that engages and increases the accessibility of the audience (Rigney, 2010). Outputs from the project are aligned with the ethos around the Open Education Resources (OER). The Higher Education Academy (2013) defines Open educational resources as …digital materials that can be used, re‐used and repurposed for teaching, learning, research and more, made freely available online... OER include a varied range of digital assets from course materials, content modules, collections, and journals to digital images, music and video clips.”

4. The gathering the voices’ website It was envisaged in the pilot stage, that a website would the ideal vehicle for disseminating such a vast and rich array of audio narrative combined with fascinating artefacts and other associated metadata objects. This seemed an ideal solution to ensure that the resources could be accessed by as wide an audience wide an audience as possible. Users would be able to click on an oral testimony, which is presented from the viewpoint of the interviewee. Once users access each ‘individual’, they are then presented with a comprehensive overview of all the associated objects with the individual, many of which are described in depth within each oral testimony, thus adding an added dimension to the testimony. Users can read the testimony transcript, listen to the audio, view all associated objects such as scanned memorabilia all of which are linked to the oral testimony. Examples of this include items such as personal photographs, personal memorabilia and other intimate objects; all of which assist by bringing the oral testimony to life through the addition of a more interactive dimension for the viewer.

459

Angela Shapiro, Brian McDonald and Aidan Johnston

Figure 1: Image from website

Figure 2: Home page of gathering the voices’ website

460

Angela Shapiro, Brian McDonald and Aidan Johnston We have already placed a number of interviews on our website, called 'Gathering the Voices’ (www.gatheringthevoices.com). Along with the testimonies, personal photographs further enhance the “authentic experience” of the website and are a primary resource of material and are unique artefacts and by placing them on the website, they will be preserved for future generations to access. Together, with the spoken voice they are a unique and irreplaceable resource. The British Library made a request to preserve the website as part of their major Library archival project, the aim of which is to “archive (websites of importance) that will be used for scholarly research in a range of disciplines (The British Library Board, 2013). However, although the website has attracted over 12,000 hits at the time of writing (2012‐2013) we were conscious that users were not staying on the website long enough to listen to the audio transcriptions which allowed the user to hear the ‘voice ‘ of the survivors, a key element to the project. We have now carried out an independent evaluation of the website to see if there are any areas on which we can improve to enhance the user experience The results from the evaluation indicated that in order to reach a wider audience base, we need to target specific user groups and improve the visibility of the website in the worldwide web search engines such as Google. This will, in part, be achieved by increasing the ability to search all of the text within oral testimony transcripts which does not currently occur. The rich value of the oral testimony will be unlocked by taking this approach as each word of testimony will be easily searchable both from the website itself and across the web through scholarly search engines such as Google scholar. Other strategies for increasing the online awareness of the project include a new social media strategy. By harnessing the power of popular social media tools such as twitter and Facebook, this will open up the resources, old and new, to a much wider community of scholars.

5. The impact of serious games Another important aspect of the second phase of the Project is to investigate the use of serious games to disseminate the information of the testimonies to a younger generation. Serious Games are computer and video games that are intended not only to entertain users, but also to have additional purposes such as education and training. A serious game is usually a simulation that has the look and feel of a game, but is actually a simulation of real‐world events or processes. Although serious games can be entertaining, their main purpose is to educate and be accessible for all ability levels. Commercial off the shelf games can be used but often it is better to create bespoke products because accurate more authentic content is sometimes required which is the case for the Gathering the Voices project. These serious games and applications will be generated using a Game Jam. A Game Jam is an event where developers can come together to create games and other digital products, these ‘jams’ typically have a compressed timeframe such as 48 hours. This key constraint with others, such as theme and platform tend to produce interesting project which sometimes are fairly innovative in terms of design or solution (Independent Games Festival, 2013). One of the most well ‐ known Game Jams is the Global Game Jam which had 319 sites in 69 Countries with 16,075 participants making 3,128 games (Global Game Jam, 2013). While most Game Jams are effectively fun events which make more commercial style projects there has been a recent growth in Game Jams for a more serious purpose such as the recent Game Jam hosted by Cancer Research UK (Cancer Research UK, 2013) the Health Game Jam (IGDA Health Game Challenge, 2010), NASA Game Jam(Dark Side of the Jam, 2013) or Jamming For Small Change(Scottish Game Jam, 2013). We acknowledge that the Game Jam may not produce finished games or applications but they have potential to solve some of the issues identify in terms of dissemination of information from the Gathering The Voices project and there is a potential that some of solutions may some commercial value (Games Sauce, 2009).

6. The next stages in the project All this has resulted in new refugees from across Scotland approaching us keen to have their family history recorded on our website. For several of these people coming forward, this is the first time they have told their story, so it is incumbent on us to ensure that their testimonies are replicated appropriately and sensitively. We are incorporating the OER philosophy in future projects both internally and externally. For example, we are developing eLearning packs together with colleagues from the Scottish schools’ sector with anticipated audience including primary and secondary school children across Scotland. Small scale development activities have commenced with staff representing five schools in Glasgow.

461

Angela Shapiro, Brian McDonald and Aidan Johnston In addition, final year students in GCU are designing and producing concepts from a prescribed brief on the production of a mobile exhibition for the GtV project. Second level students representing further education colleges and GCU are designing animations representing the journeys taken by the survivors to Scotland. Several collaborative projects together with colleagues in Glasgow Caledonian University, staff and students of The Royal Conservatoire of Scotland are working with local schools as part of their community engagement outreach programme focusing on “journeys” this is the theme of the Holocaust Memorial Day. By embracing new practices in games and software development, we hope to engage and educate current and future generations to recognise the contribution that ‘refugees’ can bring to new communities.

References Association of Jewish Refugees (2013) “About the AJR” [on line] http://www.ajr.org.uk/index.cfm Cancer Research UK (2013) “Top technology gurus to design mobile phone game to speed up cancer cures” [on line] http://www.cancerresearchuk.org/cancer‐info/news/archive/pressrelease/2013‐02‐28‐cruk‐phone‐game‐to‐speed‐ up‐cancer‐cures Dark Side of the Jam (2013) “Home Page” [on line] http://www.darksidejam.com/ Ekstrom, S. R. (2004), The mind beyond our immediate awareness: Freudian, Jungian, and cognitive models of the unconscious. Journal of Analytical Psychology, 49: 657–682 Fenge Lee‐Ann (2002) Practising partnership‐‐participative inquiry with older people, Social Work Education: The International Journal, 21:2, 171‐181 Games Sauce ( 2009) “The Global Game Jam and Beyond: PULSE” [on line] http://gamesauce.org/news/2012/12/13/the‐ global‐game‐jam‐and‐beyond‐pulse‐2009/ Global Game Jam (2013) “Home Page” [on line] http://globalgamejam.org/ Gubrium, J. F. and Holstein, J. A. (1998), “Narrative practice and the coherence of personal stories”[on line] . The Sociological Quarterly, 39: 163–187. Higher Education Academy (2013) “Open Educational Resources” [online] http://www.heacademy.ac.uk/oer IGDA Health Game Challenge (2010) “Home Page” [on line] http://www.healthgameschallenge.org/ Independent Games Festival(2013) “Mushroom 11” [on line] http://www.igf.com/php‐bin/entry2013.php?id=1005 Prensky, M. (2001) Digital game based – learning. McGraw‐Hill, New York Rigney, A. 2010, "When the monograph is no longer the medium: historical narrative in the online age ", History and Theory, vol. 49, no. 4, pp. 100‐117. Scottish Game Jam(2013) “Jamming for Small Change” [on line] http://www.scottishgamesjam.com/jam2013/ Sense Over Sectarianism (2013) “Sense Over Sectarianism2 [on line] http://www.glasgow.gov.uk/index.aspx?articleid=8780 Shapiro, A and Johnston, A (2010) “From Workshop to the Web: Reflections on the Journey in Producing Vidcasts to Enhance Student Learning” pp. 244‐ 251. In Technology Enhanced Learning, Presented at Athens, May 2010 at First International Conference, TECH‐EDUCATION 2010. Proceedings. Springer, Berlin Shapiro A and Johnston A ( 2009) “Vidcasts for the self‐directed learner.” Poster presented at 8th European Conference on e‐ Learning, University of Bari, Italy 29‐30 October, 2009. Squires, C (2008) “Experience –centred and culturally orientated approaches to narrative” In: Andrews Squire & Tamboukou, ed. Doing Narrative Research. London: Sage. pp41‐63 The British Library Board (2013) “About Us” [on line] http://www.bl.uk/aboutus/stratpolprog/digi/webarch/ The University of Michigan‐Dearborn ( 2013) “The Voice/Vision Holocaust Survivor Oral History Archive” [on line] holocaust@umd.umich.edu Winter, R, (1996) Some Principles and procedures for the conduct of action research. In Zuber‐ Skerritt (ed.) New Directions in Action Research. London: Falmer.

462

Monitoring the Concept of e‐Learning in Mind Maps of University Students Ivana Šimonová University of Hradec Kralove, Hradec Kralove, Czech Republic ivana.simonova@uhk.cz

Abstract: Collecting objective feedback is a key problem of each educational concept. New approaches to evaluation would enable teachers a deeper reflection of forming students´ knowledge. There exist various approaches to solving this problem, and the mind mapping belongs to them. The term of mind mapping appeared in 1970s in the concept introduced by Buzan who researched ways of remembering experience. In the field of education the mind mapping relates to developing meaningful learning, when a new piece of knowledge becomes meaningful to learners if in‐built in their existing knowledge structures which are understood to be identical with mind maps. The mind map as a research method was first applied in late 1970s by Novak and Åhlberg. Buzan says the mind maps are external expressions of knowledge integrated in individual´s mind. He emphasizes the mind map is not either "correct", or "incorrect", but it is always accepted in a certain context, while it could be rejected in another one. The research applying the method of mind mapping was held at the Faculty of Informatics and Management, University of Hradec Kralove, in 2012. The research objective was to monitor how students understand the term of e‐learning. The method of mind mapping was used in the less‐traditional form, when learners were provided the Khan´s eight‐dimension schema of e‐ learning and defined the dimensions reflecting their individual concept of e‐learning. The research sample included 104 respondents enrolled in the first year of Applied Informatics and Information Management study programmes. The collected data were processed by the method of frequency analysis reflecting pre‐defined criteria, covering the analysis of terms used in the respondents´mind maps from various points of views. The results mostly expressed agreement on the Khan´s concept of e‐learning. This can be highly appreciated because the concept is complex, considering e‐learning from the whole width of this phenomenon. On the other side, it should be taken into account that the respondents were students of study programmes focusing on Informatics who relate closer to this field from the point of profession and interest and pay more attention to it. For the future, similar research should be held so that to monitor the e‐learning concept of students of teachers´ training and other faculties and check whether the concept is influenced by their future profession. Keywords: higher education, informatics, mind maps, e‐learning, concept

1. Introduction To receive realistic and objective feedback and assessment of the process of instruction are the key educational problems. Approaches to searching for new ways to have a deeper reflection on how students understand problems and develop concepts call for strong efforts on one hand but on the other hand they provide a basic prerequisite for running the learning process efficiently. There exist various approaches to monitoring them, and the mind mapping (mental mapping, semantic mapping, concept mapping) is one of them. In 1970s this term was first introduced by the Canadian psychologist Buzan (2001) when discovering ways how to remember individual experience. He concluded the experience was saved in individual´s memory in the form of clusters showing mutual interrelations. In the field of education the mind mapping relates to developing meaningful learning, i.e. a new piece of knowledge becomes meaningful to learners if in‐built in their existing knowledge structures which Buzan understands to be identical with mind maps. Mind maps are used in different phases of the instruction process, e.g. for motivation, fixing, practising and assessing new knowledge etc. A mind map as a research method was first applied in late 1970s by Novak (1998). In his concept mind maps were diagrams expressing significant relations between terms in the form of statements. These were represented by links between terms and described their mutual relations. This concept was later adapted by Åhlberg (2004). Novak (1998) distinguished four ways how the mind maps can be used, i.e. (1) as learning strategies; (2) teaching strategies; (3) means to forming concept and content of single subjects and the instruction as the whole; and (4) a means of collecting information about learner´s understanding of the learning content. He also mentioned other ways, e.g. strategies towards acquiring (mastering) new learning content, evaluation etc. Then, Buzan (2010) defined mind maps as external expressions of knowledge integrated in individual´s mind. He emphasized the mind map was not either "correct", or "incorrect"; it is always accepted in a certain context, while it could be rejected in another one.

463

Ivana Šimonová If we really aim at making changes and applying new approaches within the educational system, it means that not only objectives, learning content and methods of instruction should be changed. Strong attention should be also paid to the concept‐forming process and the means applied within. Innovations in each subject are always connected to searching for, discovering, introducing, piloting and testing new means of assessment which will enable teachers to discover and understand learner´s understanding of terms and individual structure of knowledge. Currently, the information and communication technologies (ICT) can serve this purpose, e.g. electronic applications for creating and analyzing the mind maps, which are available on web pages of iMind‐Map (2011), brainstorm and mind map online (2011), Edraw Mindmap (2011) etc. can be used in this process.

2. Research design The research applying the method of mind mapping was held at the Faculty of Informatics and Management, University of Hradec Kralove in 2012. The research objective was to monitor how students understand the term of e‐learning. The method of mind mapping was not used in the traditional form, i.e. when learners create the mind map themselves, but they were provided the eight‐dimensional schema of e‐learning designed by Khan (2006, 25). Students adjusted his concept to their opinion by defining the dimensions with terms provided by Khan and the author of the research; in case of total disagreement with the Khan´s concept, they designed their own schema. The research sample included 104 respondents, the 1st‐year students (aged 18 ‐ 20 years) of the Faculty of Informatics and Management who in 2011/12 enrolled in the Applied Informatics and Information Management study programmes. The Khan´s multi‐dimensional schema replies to the question what is required for the open, flexible and distributed e‐learning. The schema is presented in two versions which are displayed in figure 1.

Figure 1: Two schemas of eight‐dimensional approach to e‐learning by Khan (2006) The versions differ in graphic presentation. In the middle of the left schema the word e‐learning is placed while the figure of the human being is centered the right schema. Khan does not explain how the central symbols should be understood, or whether they express any difference; he understands them identical. Thus both approaches can be applied as: (1) e‐learning as a learner‐oriented process, and/or (2) e‐learning as such a way of learning which enables/provides highly individualized approach to learning which is defined by each learner and is reflecting individual learning style preferences and other didactic‐psychological characteristics (i.e. requirements‐oriented learning).

3. Research methodology The research procedure followed two phases: First, to introduce the Khan´s schema to students. Before the research started, the Khan´s schema was explained to the participants. They were provided information on the content of single dimensions (i.e. what terms were included to each dimension by Khan) and how mind maps are created and used. The Khan´s structure of e‐learning includes eight dimensions as follows: pedagogical (P); technological (T); interface design

464

Ivana Šimonová (D); evaluation (in this research marked as feedback, F); management (M); resource support (R); ethical (E); institutional (I). Second, to modify the Khan´s schema. Students worked with the version with the figure in the centre. They adjusted it to their individual mind concepts by matching terms used in the Khan´s schema and eight terms provided by author‐researchers to the eight dimensions, or removing any dimension or term from the schema. Each match was described by the appropriate verb. The terms to be matched to dimensions were as follows:

analysis of objects, content and media used, analysis of participants (dimension P);

organization, methods, strategies used in the environment (T);

infrastructure design (hardware, software) (D);

design of e‐learning programme (design of pages, content, navigation, tools for testing) (F);

management (evaluation of learner´s work during the instruction using the assignments, evaluation of the learning environment (M);

resource support (learning management, ways of providing and spreading information, online support, maintenance (R);

social influence, cultural and geographical differences, differences in level of entrance knowledge, differences in accessibility to information, ethical and legal rules (E);

institutional support in the field of e‐learning services for students (I).

If the Khan´s schema as a whole did not reflect learner´s concept of e‐learning, they drew a new mind map.

4. Results, interpretation, discussion The collected data were processed by the method of frequency analysis reflecting following criteria:

Khan´s dimensions accepted in respondent´s individual concept of e‐learning;

other terms (provided by the author‐researcher) integrated into individual respondent´s

concept;

other terms (provided by the respondent) integrated into individual respondent´s concept.

4.1 Khan´s dimensions and terms accepted in respondent´s individual concept of e‐learning A dimension was completely accepted if the same terms were matched by the respondent as they had appeared in the Khan´s concept. In terms the respondent´s concept differed to some extent, the respondent matched one or several terms from those defined by Khan only. Thus the dimension was adjusted to the respondent´s concept. Providing no terms were matched, the dimension was accepted as it was. If the respondent did not accept the dimension for the concept of e‐learning, he deleted it from the schema. Results showed there was no respondent in the research group who did not make any changes in the Khan´s concept. Not a student worked with one or two dimensions only. Nearly 34 % respondents matched terms to all eight dimensions (at least one term to a dimension), while 66 % of respondents used from three to seven dimensions: 16.3 % respondents worked with seven dimensions; 15.3 % used six dimensions; 14.4 % of respondents matched terms to five dimensions and 17.3 % to four ones; 3 % of respondents used only three out of eight dimensions. When analyzing this results from the opposite point of view, i.e. which dimensions from the Khan´s schema were not accepted, the data are displayed in figure 2. The figure showed that the least frequently used dimension, i.e. the one which respondents matched fewer terms to, was the institutional dimension (I) (39.4 % of respondents). Further on, other rarely used dimensions were the ethical one (E, 30.8 %), resource support (R, 28.8 %), management (M, 22.1 %) and the technological dimension (T, 19.2 %). Remaining three dimensions were the most frequently used ones, i.e. the interface design (D, 2.9 %), pedagogical dimension (P, 6.7 %) and evaluation (F, 16.3 %). Thus the results show that

465

Ivana Šimonová nearly all respondents (97 %) understand e‐learning from the point of interface design (D), which relates to the respondents´ study programme, i.e. Applied Informatics and Information Management. Respondents also considered the pedagogical dimension of e‐learning (P, 93.3 %) and the evaluation role (F, 83.7 %), followed by the technological dimension (T, 80.8 %), the dimension of management (M, 77.9 %) and resource support (R, 71.2 %). One term at least was matched to the ethical dimension (E) by 69.22 % of respondents and to the institutional dimension (I, 60.6 %). At the time of cheating been considered a common feature by many Internet users, this result proved respondents´complex approach to e‐learning. These figures likely reflected the research sample structure (students of IT study programmes) as well as the fact the respondents had rather wide experience in e‐learning. At the time of running the research they had studied or had been studying at least ten online courses in the LMS Blackboard which are used for support both the face‐to face and distance lessons at their faculty. The detailed analyses discovered that 17.3 % of respondents did not match terms to the couple of dimensions, i.e. the institutional and ethical one. This couple (I, E) also appeared in combination with other rarely used dimensions, i.e. resource support (R, 6.7 %) and management (M, 3.8 %), evaluation (F, 3.8 %) and technological dimension (T, 2.9 %). Other rarely used dimension was the ethical one (E) in combination with resource support (R, 15.4 %), evaluation (F, 10.6 %), management and evaluation (M, F, 3.8 % each).

Figure 2: Khan´s dimensions from the point of respondents´acceptation

4.2 Other terms (provided by the researcher) integrated into individual respondent´s concept Resulting from brainstorming in the pilot group of 36 respondents eight other terms except the Khan´s ones were selected: fun, boredom, stress, motivation, role of designer, responsibility, autonomy, loneliness. Respondents matched them to the Khan´s dimensions. Results showed that 50 % of respondents matched all eight terms to a dimension while 9 % used four or fewer ones; five and seven terms were implemented in the schema by 16 % of respondents and 10 % of them used six terms. Results are displayed in figure 3. From the above listed terms the most frequently matched was learner´s responsibility – the value of 108 % reflects the situation when the term was matched by many respondents (but not all 100 % of them) and was matched to several dimensions. Motivation had a strong position of 84 % followed by equal result in learning autonomy and role of designer (80 %). These values show that respondents understand the importance of motivation and appreciate both the “freedom” given them by autonomous learning (the responsibility was mentioned on the first position) and at the same time they are aware of the designer´s role, which closely relates to their field of study. Last three items might result from the individualization of e‐learning which used to be expected a strength. Our results discovered stress and boredom appeared with nearly half of respondents and might correspond to the least frequently mentioned loneliness within the course of study. Results are displayed in figure 4.

466

Ivana Šimonová

Figure 3: Number of other terms used Figure 4: Frequency of other terms used

4.3 Other terms (provided by the respondent) integrated into individual respondent´s concept We are aware the list of possible terms to be matched to the dimensions is endless. Despite this fact only 11 % of respondents included additional terms to the Khan´s dimensions. Three and seven terms were added by one respondent, while one, four and six terms were included by two respondents and other three students used two terms. The results are displayed in table 1. Table 1: Number of other terms added by respondents Number of terms (n) Added by respondents (%)

0 89

1 2

2 3

3 1

4 2

5 0

6 2

7 1

The terms added by respondents related to two main fields:

education, including terms school, teacher, results, knowledge, performance, success, obligation, translation;

IT profession, covering terms PC, programme, work.

But there exist other terms which were not mentioned by Khan and are firmly connected to e‐learning: learning, tutor, communication. We expected they should appear within this step but our expectations did not prove. One of the terms was added to the individual concept by 13.5 % of respondents; not a single respondent included two or three terms. The frequency is displayed in table 2. Table 2: Terms of learning, tutor, communication implemented in individual mind maps Term Learning Tutor Communication

Added by respondents (%) 7.7 3.9 1.9

There is not a direct explanation of this state. Despite respondents were aware of the pedagogical dimension of e‐learning (see 4.1), they did not reflect this fact under this criterion. As IT students, they may be more interested in ICT tools working within the e‐learning (i.e. ICT‐supported process of forming knowledge), they feel them as technical and technological means, not activities having people behind them. Numerous students work as programmers during their studies so they know both the hardware and software are outputs of human activities and work been managed by people, but they do not reflect this fact in their concepts of e‐ learning. Working with the right‐side image (see figure 1) they may understand the figure in the centre to be a learner, not the tutor or communication manager (and Khan did not define the figure´s role).

5. In conclusion It is hardly possible to sum up all the collected data to a single and homogenous conclusion. Students´approaches mostly reflect agreement on the Khan´s concept of e‐learning, which can be appreciated,

467

Ivana Šimonová because this concept is complex, considering e‐learning from the whole width of this phenomenon. On the other side, it should be taken into account that the respondents were students of study programmes focusing on Informatics who relate closer to this field from the point of profession and interest and pay more attention and deeper inside in the field. Despite there are not numerous supporters, mind mapping is considered to be an efficient educational tool by them. It supports the process of learning by helping students collect and structure information, create concepts by making connections between, among, within concepts and identify those missing. There exist several reasons why the mind maps should be used in the process of teaching/learning:

they enable to organize and structure vast amounts of information;

being visual tools, they allow learners to process, understand and retain information in a way that accommodates their style of learning;

they foster creativity by making both existing and missing connections clearly visible;

they foster sharing ideas and collaboration between students and teachers;

they are also available in the electronic form so they can be easily created and shared.

Using mind maps for educational purposes enables teachers to motivate learners for studying, to take notes, summarize learning contents and organize study materials, to manage their own course of study and/or evaluate learners´ knowledge. In several countries (France, Finland, UK, Vietnam) the mind maps have been included in educational curricula. This mearure has not been applied in the Czech Republic yet but supporters of this tool have been using them. This tool having been tested in other researches (e.g. as proposed below) it might widen the application and become a powerful and insteresting tool on all levels of the Czech education system (Prokša, 2001). For the future, similar researches should be held so that to monitor the e‐learning concept of students of teachers´ training and other faculties and check whether their approach to e‐learning is influenced by their future profession, i.e. first, whether respondents emphasize the pedagogical dimension of e‐learning as the Informatics students do with dimensions closely relating to information technology, and second, how the didactic approach is reflected in the e‐learning concept (Klímová, 2011). The process of e‐learning implementation within the higher education in the Czech Republic was fast. Originally great expectations and hypotheses on radical increase of learning efficiency, i.e. high increase in knowledge after the ICT‐supported teaching and learning, have not always been verified in practice. That is why other aspects have been emphasized in which advantages of e‐learning are evident and not open to doubt. Primarily, it is an important positive influence on the affective domain of cognitive and learning processes, i.e. learners´motivation to learn, deepening the relation between instruction and students´future success on the labour market, as well as providing efficient study management and organization

in the macro‐structure, covering study programmes;

in the mezo‐structure, complementaring instruction and learning in a group;

in the micro‐structure, influencing learning processes of an individual.

Mind maps have reached a stable position in this structure, as it was verified by the final project research results (Šimonová, 2013).

Acknowledgements The paper is supported by the Excellence project N. 2208.

References Anonymous. “Brainstorming made simple”, [online], https://bubbl.us/. Anonymous. “Edraw mindmap”, [online], http://www.edrawsoft.com/freemind.php. Anonymous. “How to make a mind map”, [online], http://www.mind‐mapping.co.uk/make‐mind‐map.htm. Åhlberg, M. (2004) Varieties in concept mapping, [online], http://academia.edu/829330/ varieties_ of_concept_mapping. Ausubel, D.P. (1968) Educational psychology. A cognitive view. Holt, Rinehart and Winston, New York. Buzan, T. (2001) The power of creative intelligence. Thorsons, London.

468

Ivana Šimonová Buzan, T. (2010) The mind map book. Unlock your creativity, boost your memory, change your life. Pearson BBC Active, New York. Buzan, T. “Think Buzan”, [online], http://blog.thinkbuzan.com/education/beginner%E2%80%99s‐guide‐to‐the‐use‐of‐mind‐ maps‐in‐elementary‐schools. Khan, B.H. (2006) E‐learning ‐ osem dimenzií otvoreného, flexibilného a distribuovaného e‐learningového prostredia. SPU, Nitra. Klímová, B. and Poulová, P. (2011) “Tutor as an important e‐learning support“, Procedia Computer Science, 1st World Conference on Information Technology (WCIT2010), Bahcesehir University, Istanbul, Turkey, Vol 3, October. Book Editor(s): Karahoca, A. and Kanbul, S. Novak, J.D. (1998) Learning, creating, and using knowledge. Concept maps as facilitative tools in schools and corporations. Lawrence Erlbaum Associates, Mahwah. Prokša, M. (2001). Pojmové mapy jako prostředek zpětnévazby. In Bílek, M. (2001). Psychogenetické aspekty didaktiky chemie, Hradec Kralove, Gaudeamus. Šimonová, I. (2011) “Pojmové mapování jako zpětnovazební prostředek. Část 1”, Media 4U magazine, Vol 9, No. 3, pp 12‐ 20 [online], http://www.media4u.cz/mm032012.pdf. Šimonová, I. (2013) “Concept of e‐learning reflected in mind maps of university students“, Paper read at 5th World Conference on Educational Sciences. Roma, Italy, February. Printed.

469

Impact of Internet Usage on Students’ Academic Performance Florica Tomos¹, Christopher Miller¹, Paul Jones², Ramdane Djebarni¹, Oshisanya Oluwaseyi Olubode¹, Peter Obaju‐Falade¹, Henrietta Eleodimuo Nkiruka¹ and Tejaswi Asmath¹ ¹South Wales University, Pontypridd, UK ²Plymouth University, UK florica.tomos@southwales.ac.uk christopher.miller@southwales.ac.uk paul.jones@plymouth.ac.uk djebarni@glam.ac.uk Abstract: This study will explore the impact of internet usage on students’ performance. Using a model adapted from Norzaidi and Salwani (2009), task‐technology fit model, this research will investigate the impact of internet usage, technology satisfaction and technology resistance on students’ academic performance and will explore whether the independent variables predict students’ academic performance. The research was organised at a Higher Education Institute in Wales and 120 questionnaires were distributed to undergraduate year 1 and 2, graduate and postgraduate students. This study used the following techniques: factor analysis, multiple regression and correlation. The results from the regression analysis of this research indicate that technology resistance it does predict students’ academic performance. Furthermore, there is a significant positive association between students’ academic performance, internet usage, technology resistance and technology satisfaction. The study built on a previous model in educational system in Malaysia and adapted to a Higher Education Institute in Wales. It focused on the impact of internet usage and technology resistance upon students’ academic performance. The model is trying to incorporate an adopted structure from another educational system into a Higher Education Institute in Wales and to increase the effectiveness of internet usage in higher education. The results of this research can be adopted by other Higher Education Institutes in the UK. This study will be beneficial for students as well as for higher education institutions by increasing the effectiveness of services. The study is bringing a contribution to the development of students’ satisfaction by eliminating the barriers (technology resistance) in accessing technology and will increase students’ potential to use the internet. The study explores a very actual topic by addressing the internet usage in Higher Education (HE) Keywords: students’ satisfaction, academic performance, internet usage, technology satisfaction and technology resistance

1. Introduction Internet usage has been recognised as a powerful resource for students’ assignments and academic performance (Jefferries and Hussain, 1998; Norzaidi et al., 2007a, b, cited in Norzaidi and Intan Salwani, 2009).Despite of the existent research there remain gaps in knowledge regarding how students in Higher Education (HE) use the internet for academic work (Aiken et al., 2003 cited in Norzaidi and Intan Salwani, 2009).Thus, the following issues such as internet usage, technology satisfaction, technology resistance can drive the question of whether HE institutions by investing in internet, are aware of the internet usage and its effectiveness (Norzaidi and Intan Salwani, 2009). The aim of this study is to explore the impact of internet usage on students’ academic performance.The objectives of this research are:

To identify barriers to students’ satisfaction regarding the access of internet (technology resistance);

To investigate the relationship between internet usage and students’ academic performance in HE;

To evaluate the relationship between internet usage, satisfaction with technology, technology resistance and students’ academic performance.

This study builds on the prior work of Norzaidi and Intan Salwani (2009) in exploring and explaining the impact of internet usage on students’ academic performance.Furthermore, the study is based on the students’ perceptions about the influence of the internet usage on their academic performance.A set of variables were used within the study to explain how well the internet usage relates to students’ academic performance, such as: students’ academic performance (dependent variable), internet usage, technology satisfaction and technology resistance (independent variables). A conceptual framework has been designed and potentially will be developed into a research model.As mentioned, this was based on and adapted from Norzaidi and Intan Salwani (2009), in order to analyse the above variables.The study will explain how the internet usage determines students’ academic performance by

470

Florica Tomos et al. exploring students’ perceptions and their perceived future academic performance.The model will be a simplified version of more complex models developed by Goodhue and Thompson (1995), DeLone and McLane (1992), Norzaidi et al. (2008 a,b,c) and Norzaidi and Intan Salwani (2009).Nevertheless, the research attempts to investigate into technology resistance from another angle, by answering to new and different questions.The study will endeavour to answer to following research questions:

Does the internet usage predict students’ academic performance?

Does technology satisfaction predict students’ academic performance?

Does technology resistance predict academic performance?

Is there a relationship between internet usage, technology satisfaction, technology resistance and students’ academic performance?

2. Literature review The Internet was defined as an effective tool suitable for academic purpose and aiming to enhance students’ capabilities (Awais et al., n. d.).Nowadays, they said, the internet has a huge impact on every aspect of life such as: business, social activity, communication and education. They also stated that from students’ perspective, online learning is multidimensional (Awais et al., n. d., p. 102).These authors searched for the meaning of the word “internet” and found that its root is coming from two words: “international” and “network”.Thus, they agreed to define the Internet as: “...an international computer network of information available to public through modem links” (Awais et al., n. d., p. 102). It can be argued that the internet is used to communicate either internally between students and tutors within HE institutions or externally with other institutions, friends and colleagues.The Internet is a fast and continual moving, a space without borders, described as “a sea of information” (Awais et al., n .d., p. 102).Even though these authors raised the questions regarding the benefits of using the internet, the factors affecting the internet, the impact of internet on students’ learning and the need to encourage a positive attitude towards internet usage, there is still a gap in knowledge regarding the impact of internet usage on students’ academic performance, which this study will try to explore and answer.Many authors stated that the introduction of information technology (IT) and mainly the internet will have a positive impact on the students’ interest, attention and possible on their performance (Chandler, 2002; Palloff and Pratt, 1999; and many other authors cited in Awais et al., n. d. ).However, Doppelt et al. (2009) argues that the internet has no impact on student achievement. Limayem and Cheung (2011) suggested that the continued use of the internet will be the result of a “habit”.Additionally, they stated that internet technologies will help education and training, but mostly it will help students to learn at their own pace.Limayem and Hirt (2003) said that the instructors (teachers) when using the internet must be confident about the satisfaction and perceived usefulness of the internet by their students (Limayem and Cheung, 2011).Furthermore, McGill and Hobbs (2008) discovered that instructors were less satisfied regarding the assistance they received with their teaching from the virtual learning environment (VLE), whilst students’ perceptions regarding the learning support from VLEs were higher. Other authors stated the importance of information technology (IT) in higher education and claimed that this will become an important part of HE (Selwyn, 2003; Suppes, 1996, Luchrmann, 1971, cited in Selwyn, 2003, p. 1). Maeroff (2003) argued that the online learning is a “sea change”, a mix of knowledge and technology that allow any HE student to learn anytime and in anyplace.After the investigation and analysis of students’ engagement in online learning, the authors believed that it increased their educational experiences (Kuh, 2001; Aggarwal and Bento, 2000; Maeroff; Pittinsky, 2003, cited in Robinson and Hullinger, 2008).Similarly, Brotcombe (2005) claimed that IT is allowing a huge transformation in the HE system, within the new “knowledge society”. A study conducted in Malaysia by Norzaidi et al. (n. d.) demonstrated that both internet usage and technology satisfaction have a positive influence upon managerial performance.Thus, it could be suggested that there is also a positive relationship between internet usage, technology satisfaction and students’ academic performance.However, there is not a definite result due to other researchers who found no relationship between internet usage, technology satisfaction and managerial performance (Lawrence and Low, 1993; Mawhinney and Lederer, 1990; cited in Norzaidi and Intan Salwani, 2009).According to this study, the internet

471

Florica Tomos et al. is a continual learning within a learning community.To emphasise the second view of the above research, evaluating the impact of learning community on student achievement, Doppelt (2009) found that there is no effect on student achievement (Cohen and Hill, 2000; Kennedy, 1999 cited in Doppelt, 2009).Likewise, Tavares et al. (2004) argued about a new approach to teaching, a student‐centred learning approach.This approach emphasises the internet usage as an innovative way of developing students’ autonomy.Equally important is the student attitude and his/her perceived satisfaction regarding the internet usage.This study argues that the internet is “hands‐on experience”. According to Alshare and Lane (2011) the hands‐on experience should provide students’ satisfaction when using the internet.Moreover, the results of their study emphasise the importance of previous experience of students’ satisfaction.On the contrary, Kuh stated that student academic level and engagement has a direct impact on his/her academic development (2003, cited in: Robinson et al., 2008). It could be concluded that students’ academic performance would be a direct result of how much work and involvement they have when accessing and using the internet. Awais et al. (n. d.) argues that students’ academic performance and technology satisfaction are fostered by the training and the technical support received from the institution’s infrastructure.Additionally, Norzaidi and Intan Salwani (2009) emphasise the role of updated information on the internet/intranet as an issue which determines either technology satisfaction or technology resistance.In the meantime, other authors argued about the existence of an anxiety when accessing the internet which causes people to avoid the use of information communication technology (ICT) (Shashanni, 1993; Colley at al., 1994, cited in Selwyn, 2003). Based on the literature review this study developed a research framework and proposed the following four hypotheses to be tested: H1: “The internet usage predicts students’ academic performance in Higher Education” H2: “Technology satisfaction predicts students’ academic performance” H3: “Technology resistance predicts academic performance” H4: There is a relationship between internet usage, technology satisfaction, technology resistance and students’ academic performance. The research framework designed by this study and based on previous complex model is as follows:

STUDENTS’ PERFORMANCE

INTERNET USAGE

TECHNOLOGY RESISTANCE

TECHNOLOGY SATISFACTION

Figure 1: Research framework

3. Research methodology This is an empirical positivist and hypothesis testing study with a self‐completion questionnaire, based on a research framework with four theoretical variables: students’ academic performance, internet usage, technology satisfaction and technology resistance. Therefore, a positivist survey was designed to collect primary data from a sample of 125 students. To collect the survey data, the research designed a structured questionnaire with 20 open and closed questions. The targeted population was undergraduate, graduate and postgraduate students in a HE institution in Wales, from the following faculties: 1 – Business, 2 ‐ Humanities, 3 – Technology, 4 – Art & Media, 5 – Sports & Health, 6 – Science. To reflect the population accurately, the research decided that a sample of 120 respondents would be appropriate and would be considered representative for that population and suitable for the analysis. The chosen technique for the sample was simple random sample where each student had the chance to be included in the sample.

472

Florica Tomos et al. The questionnaires were distributed to students and gathered information directly by asking questions about students’ academic performance, internet usage, technology satisfaction, technology resistance and demographic data, grouped accordingly into five categories, codified and used responses as data for analysis. Before completing the questionnaires, the students were told the purpose of the research and assured confidentiality. The questions were also designed to ensure that they correspond to the above objectives: the identification of barriers and students’ satisfaction regarding the internet access, the investigation of the relationship between students’ academic performance in higher education and technology satisfaction/internet usage, the evaluation of the relationship between internet usage, satisfaction with technology, technology resistance and students’ academic performance. The study selected more students than were required to ensure that there were a sufficient number of students for the research. Of 125 questionnaires, 120 (96%) responses were obtained. The response rate was 96%, which demonstrated that the sample was not bias (Roscoe, 1975 cited in Norzaidi and Intan Salwani, 2009). The questionnaire included five categories of questions addressing four hypotheses formulated in the study. The first category contained eight questions which were addressing the issues about internet usage. The second category addressed two questions regarding the second variable ‐ technology satisfaction. The third group of questions comprised issues such as technology resistance and addressed three questions. The fourth group of questions comprised three questions and addressed issues regarding the dependent variable – students’ academic performance. Finally, the fifth group of questions contained four questions and addressed demographic data such as: gender, age, nationality and course, which were regarded as moderating variables. The study ensured that all the questions were clear and well formulated to avoid misunderstanding. Some items from first and second group of questions were using grid and a five‐point Likert scale from 1 = strongly disagree to 5 = strongly agree (Q1, Q3 and Q4 – grid and rating scales – Likert scale). The study also used ranked and multiple choices questions (Q16 and Q14). Mainly the study used nominal and ordinal scale. The questionnaires were distributed to students in the last week before the holiday. Although there was no indication either all the questions were compulsory, the students completed all questions. The questionnaire was designed based on and adapted from four prior studies: Tavares, J. et al. (2004), Uskov & Uskova (2003), Wang (1993) and Norzaidi & Intan Salwani (2009). Furthermore, to discover whether the measures developed within this study were effective, the study proceeded to items analysis. Thus, the measurement of the total number of items was another aspect of the research. Therefore, an issue of the study was to find the scale’s internal consistency, more precisely to find the accuracy in measurement or reliability. To find out if the variables of the research were linked together and measure the same theoretical variable (students’ academic performance), the study used a very common indicator of internal consistency – Cronbach’s alpha coefficient (De Velles, 2003 cited in Pallant, 2007). Different studies suggested that this coefficient should be above 0.7, so that the scale of the study will be considered as reliable. Authors also argued that Cronbach’s alpha coefficient is very sensitive to the number of items involved in the research. In this case the study had 24 items. The research discovered that the Cronbach’s Alpha value was 0.693. This value was sufficient and therefore the items were reliable and able to provide accuracy within the final results of the research. This also provided the study with the information that there was an acceptable and good internal consistency and a reliable scale with this sample (Pallant, 2007). Table 1: Reliability statistics Cronbach’s Alpha 0.693

Cronbach’s Alpha Based on Standardised Item 0.810

Number of Items 24

SPSS – The impact of internet usage on students’ academic performance ‐ 2012

4. Data analysis This study explored the students’ perception/attitude regarding the internet usage and its impact on study performance. The research analysed whether there was a relationship between students’ technology satisfaction, internet usage, technology resistance and students’ academic performance. Particularly, the research identified whether any of the following: internet usage, technology satisfaction and technology resistance predict students’ academic performance. The study analysed if there was a positive impact of the internet usage, technology satisfaction and technology resistance upon students’ academic performance. To analyse the data and address the research questions and test the hypothesis, the study used a few statistical techniques. Thus, the first technique employed by the study was factor analysis. According to Pallant (2007)

473

Florica Tomos et al. this technique does not test the hypotheses but it provides a reduction technique for the data. In this study factor analysis technique reduced a large number of items/questions such as 24 questions/items to a smaller and more manageable number of items such as four, in order to carry out other techniques such as linear regression. Factor Analysis technique involved a procedure and followed three steps: the assessment of suitability of data for factor analysis technique, factor extraction and the last step factor rotation and interpretation. Tabachnick and Fidell (Pallant, 2007) recommended larger sample and they argued that factors obtained from small sets of data cannot be generalised easily. Stevens (Pallant, 2007) suggested that a sample with 120 respondents might be considered as sufficient for generalization (the ratio of this study is 30:1, greater than the recommended one – 10:1). According to Pallant (2007) Bartlett’s Test of Sphericity is less than 0.05 (Table 4). From the analysis of Correlation Matrix (Table 2) most of the factors are less than 0.3 or around 0.3. In the same way, KMO index was within the recommended range with a minimum of 0.6 (Table 7). This acknowledged us that the technique was appropriate. The second step followed by the study within the factor analysis technique is factor extraction by using Principal Component Analysis. The phase of the analysis attempts to determine the smallest number of factors that can be used to depict the interrelations between the variables within a group and it employs different approaches of extracting a particular number of factors. The study attempted to experiment with different number of factors until the appropriate solutions were found. According to this phase it seems that the most relevant number of factors for each group of variables either dependent or independent is as follows:

Group 1/ Internet Usage (Independent variable) ‐ the study extracted the following factors: Academic Information, Communication with Tutors, Frequency of using Campus Network and Where do you access the Internet – at University;

Group 2/Technology Satisfaction (Independent Variable) ‐ the study extracted the following factors as appropriate: Attitudes/I find using the PC easy; Attitudes/I like using the PC; Attitudes/Using the internet increases my study performance;

Group 3/Technology Resistance (Independent Variable) ‐ the study extracted the following factors: Tools used to access the internet; Additional training to use the Internet;

Group 4/Students’ Academic Performance (Dependent Variable) – the study extracted the following factors: The level of degree expected; Using technology is enhancing your academic performance.

The last step of the procedure involved factor rotation and interpretation. After determining the number of factors for each group of variables, to ease the interpretation process, the factors were rotated by using an oblique technique – Direct Oblimin. According to Tabachnick and Fidell (Pallant, 2007) it seems that the oblique technique is more difficult to interpret than the orthogonal technique which is easier for the interpretation. Nevertheless, whilst the orthogonal technique incorrectly attempts to start with the presumption that the variables are not correlated, the oblique technique allows the factors to be correlated. It seems that this is the advantage of using such technique. Additionally, the Correlation Matrix shows the strengths between the two factors. For example, a higher value demonstrated that the components are related. Thus, the items from technology satisfaction are strongly correlated, also the items belonging to internet usage and finally the items belonging to technology resistance group. Finally, “Communalities” provide information about how much variance within each factor is explained (Table 7). A high value explained that the items fit well with the other elements within its component. Thus, the study has good communalities with high values for Technology Resistance (0.620), Technology Satisfaction (0.832; 0.850; 0.623), Internet Usage (range between 0.455 – 0.847) and Students’ Academic Performance (0.671) proving that the items fit well within its component (Table 7). The second statistical technique used within this research is regression analysis – multiple regressions. This type of statistical technique allowed the study to use the best set of variables to predict the dependent variable and to check on the existence of any correlations between the dependent and the independent variables (Table 3). From the “Correlations” Table it could be seen that there are correlations between the dependent variable – Students’ Academic Performance and one independent variable – Technology Resistance. The relationships are significant as the proposed level is 0.314 which is above the minimum level 0.3. The first stage of the analysis also advises to check on the correlation of each of the independent variables not to be too high. It has been suggested that the bivariate correlation should not be more than 0.7. Thus it could be observed that there is a good correlation of 0.349 between two independent variables: Technology

474

Florica Tomos et al. Satisfaction and Internet Usage. This result suggests that the existence of a positive attitude towards internet and PC (computers) which at its turn has a positive impact upon the increase of internet usage. A second good correlation is between the two independent variables – Internet Usage and Technology Resistance and is showed by Pearson Correlation, r = 0.314 (Table 3). The statistical analysis of this research is testing the significance of the relationship between the variables from the research model. Within the statistical analysis, the technique of correlation analysis in the study depicts only the strength and the direction of a linear relationship between two variables and it does not describe the factor/variable which causes the phenomena (Pallant, 2007). The research found that Pearson Correlation (r) is 0.314 indicated the existence of a medium positive relationship between Student Academic Performance and Technology Satisfaction. Additionally, further information was given by Sig value which is 0.001. Equally important are the results from Table 2 indicating that there is a medium relationship between Technology Satisfaction and Internet Usage (r = 0.349, Sig = 0.000; the correlation is significant at the 0.01 level). Furthermore, there is also a medium relationship between internet usage and technology resistance displayed by the Pearson correlation value, r = 0.313 with Sig = 0.001, where correlation is significant at the 0.01 level. Table 2: Correlations TechSatisFA1

InternetUsageFA1

TecgResFA1

StudPerfFA1

TechSatisFA1 InternetUsageFA1 TecgResFA1 Pearson Correlation 1 .349** .216* Sig. (2‐tailed) .000 .019 N 120 120 118 Pearson Correlation .349** 1 .313** Sig. (2‐tailed) .000 .001 N 120 120 118 Pearson Correlation .216* .313** 1 Sig. (2‐tailed) .019 .001 N 118 118 118 Pearson Correlation .119 .139 .314** Sig. (2‐tailed) .198 .132 .001 N 119 119 117 **. Correlation is significant at the 0.01 level (2‐tailed). *. Correlation is significant at the 0.05 level (2‐tailed).

StudPerfFA1 .119 .198 119 .139 .132 119 .314** .001 117 1 119

As part of the regression procedure, there is another stage called “collinearity diagnostics” on the variables (Table 5). The two values of Tolerance and VIF demonstrate that multiple correlations with other variables are low. Furthermore, the study demonstrated that the results are statistically significant Sig = 0.007 < 0.05 (Table 6). Table 3: Correlations among the construct – Pearson correlation

Students’ Academic Performance Internet Usage Technology Satisfaction Technology Resistance

Students’ Academic Performance 1.000

Internet Usage

Technology Satisfaction

Technology Resistance

0.139

0.119

0.314

0.139 0.119 0.314

1,000 0.349 0.313

0.349 1,000 0.216

0.313 0.216 1,000

Adapted from Norzaidi and Intan‐Salwani (2009) Table 4: Exploratory factor analysis results Construct

Technology Resistance Students’ Academic Performance Internet Usage Technology Satisfaction

Kaiser‐Meyer‐Olkin Measure of Sampling Adequacy 0.500 0.500

Bartlett’s Test of Sphericity/Sig.

Percent of total variance explained (%)

0.009 0.000

61.98 67.13

0.639 0.671

0.000 0.000

49.41 76.97

Adapted from: Norzaidi and Intan‐ Salwani (2009). Correlation is significant at the 0.05 level.

475

Florica Tomos et al. Table 5: Regression – coefficients Model (Constant) Internet Usage Technology Satisfaction Technology Resistance

Standardized Coefficients β t Sig 0.033 0.974 0.031 0.316 0.753

Correlations Partial Part ‐ ‐ 0.030 0.028

Collinearity Statistics Tolerance VIF ‐ ‐ 0.819 1.222

0.044

0.463

0.644

0.044

0.041

0.865

1.156

0.295

3.118

0.002

0.281

0.278

0.889

1.125

Adopted from Norzaidi and Intan Salwani (2009) Table 6: Model summary and ANOVA Model

R Square

Adjusted R Square

Sig

1 Regression

0.320 ‐

0.102 ‐

0.078 ‐

‐ 0.007

Adopted from Norzaidi and Intan Salwani (2009) Drawing on the findings from the data analysis, which were based on the respondents’ answers, it seems that they confirm the hypotheses of the research. Additionally, these findings support Norzaidi and Intan Salwani (2009) research which suggests that the internet usage has a positive impact on the students’ academic performance. As opposed to Norzaidi and Intan Salwani (2009) research, the new findings of this study prove that the technology resistance is a predictor of students’ academic performance. It was also revealed that students with effective access to campus network will be satisfied if additional training will be provided by the university infrastructure. This was demonstrated by the positive moderated association between technology resistance and technology satisfaction. About 87.5% of the respondents agreed and strongly agreed that they accessed the internet on university campuses for academic purpose. This result demonstrated an effective use of campus network and at the same time an effective service provided by the university campuses. At its turn, a good service offered by the university could lead to increase in university standards towards international level and also toward performance management.

5. Conclusions and recommendations This research paper contributes to the existing body of knowledge in terms of filling the gap and also acknowledging the existence of a causal effect between technology resistance and students’ academic performance. Moreover, building on previous studies (Norzaidi and Intan Salwani, 2009) this research demonstrated the viability of the hypothesis that there is a relationship between internet usage, technology resistance, technology satisfaction and students’ academic performance. In conclusion, the key findings from this empirical analysis are as follows:

It was demonstrated in this study that the respondents perceived the internet to be an effective mean which contributes to the increase of their academic performance (Figure 2 & Table 8). However, the research revealed that about 20% of the respondents need additional training either for accessing resources or technical training.

It was perceived that the lack of training and tools used to access the internet could be a barrier to effective use.

The correlation analysis revealed a significant positive relationship between additional training to access the internet, the tools used to access the internet and the perceived belief that technology enhanced performance and the degree perceived to achieve (Technology Resistance and Student Academic Performance: r = 0.314, Sig = 0.001) (Table 2).

Additionally, the study depicted a second significant positive association between communication with tutors, the reason for accessing the campus internet – academic information and the perceived degree or additional training (Internet Usage and Technology Resistance: r = 0.313, Sig = 0.001) (Table 2).

476

Florica Tomos et al.

Finally, the results demonstrated the association between the perceived attitude towards the access of internet (like/dislike), the place of access (university), the purpose (academic information) and communication (tutors). (Technology satisfaction and Internet usage: r = 0.349, Sig = 0.000)

This paper is aiming to help university in finding solutions to improve the services provided to students towards international standards in the context of knowledge society. However, given the limited sample size, the interpretation of the survey results may not be ready for generalization. Therefore, it is recommended future studies to design a bigger sample size, across universities and cultures. Table 7: Factor analysis Correlation Matrix KMO Measure of Sampling Adequacy Communalities Extraction Variance Explained % Component Matrix Sig

TechResFA1 0.240 0.500

StudPerfFA1 0.343 .0500

TechSatisFA1 0.573 0.671

InternetUsageFA1 0.444 0.639

0.620

0.671

0.850

0.508

61.98 0.787 0.009

67.13 0.819 0.000

76.97 0.922 0.000

70.64 0.713 0.000

Extraction Method: Principal Component Analysis Table 8: Using the internet is increasing my study performance For how long have you been using the internet? 1‐3 yrs

yrs >7 yrs Total

Agree & Strongly Agree

Neither

7 14 64 85

1 ‐ 7 8

Disagree & Strongly Disagree 2 3 22 27

Figure 2: Internet usage and students’ performance

References Alshare, K. A. and Lane, P. L. (2011) Predicting Student‐Perceived Learning Outcomes and Satisfaction in ERP Courses: An Empirical Investigation. Communications of the Association for Information Systems. Vol. 28, No 34, pp 571‐584. Awais, Bilal, Usman, M., Waqas, M. and Sehrish. (n. d.) Impacts of Internet Usage on Students’ Academic Performance (CGPA). Research Paper. [Online]. www.Google [Accessed Date: March 2012]. Brotcombe, P. (2005) Making Sense of Internet: Exploring Students’ Use of Internet‐based Information Resources in University. Paper presented at the British Educational Research Association Annual Conference. 14‐17 November 2005. UK: University of Glamorgan. Bryman, A. and Bell, E. (2011) Business Research Methods. 3rd ed. USA, New York: Oxford University Press Inc. Djebarni, R. (2012) Research Methods. Lecture. [Online]. www.glamlife.ac.uk/Blackboard [Accessed date: March 2012].

477

Florica Tomos et al. Doppelt, Y., Schunn, C. D., Silk, E. M., Mehalik, M. M., Reynolds, B. and Ward, E. (2009) Evaluating the impact of a facilitated learning community approach to professional development on teacher practice and student achievement. Research in Science and Technological Education, Vol. 27, No. 3, pp 339‐354. Limayem, M. and Cheung, M. K. (2011) Predicting the continued use of Internet‐based Scholar.learning technologies: the role of habit. Behaviour and Information Technology. Vol. 30, No. 1, pp 91‐99. McGill, T. J. and Hobbs, V. J. (2008) How students and instructors using a virtual learning environment perceive the fit between technology and task. Journal of Computer Assisted Learning. Vol 24, No. 3, pp 191‐202. Norzaidi, M. D. and Intan Salwani, M. (2009) Evaluating technology resistance and technology satisfaction on students’ performance. Campus Wide Information Systems. Vol.26, No. 4, pp 298‐312. rd Pallant, J. (2007) SPSS – Survival Manual. 3 ed. USA, New York: Open University Press, McGraw‐Hill Education. Robinson, C. C. and Hullinger, H. (2008) New Benchmarks in Higher Education: Student Engagement in Online Learning. Journal of Education for Business. November/December 2008. Heldref Publication. JISC [Online]. [Accessed date: 20.01.12]. Saunders, M., Lewis, P. and Thornhill, A. (2007) Research Methods for Business Students. 4th ed. Essex: Pearson Education Ltd. th Sekaran, U. and Bougie, R. (2010) Research Methods for Business. 5 ed. UK, West Sussex: John Wile & Sons Ltd. Selwyn, N., (2003) Why Students Do (and Do Not) Make Use of ICT in University. Proceeding of Conference: Finding Common Ground: IT Education, Dearing and Democracy in the Information Society. July 9th 2003. Leeds: University of Leeds, Department of Computing. Tavares, J., Pereira, A., Cabral, A. P., Carvalho, R., Fernandes, C., Huet e Silva, J. and Monteiro, S. (2004) Facing New Higher Education Challenges Under and Postgraduate Web based Courses. Paper presented at the European Conference on Educational Research. 22‐25 September 2004. Crete. University of Crete. Uskov, V. and Uskova, M., (2003) Application of Telecommunication in Education: National Science Foundation. Projects on Advanced Technological and Online Education in Information Engineering Technology. [Online]. IEEE. www.glam.ac.uk/ [Accessed Date: 18.01.2012]. Wang, M. H. (1993) Electronic Communication in a University Campus. International Professional Communication Conference. 1993. National Chiao Tung University, Hsinch, Taiwan. [Online]. www.glam.ac.uk/ [Accessed Date: 18.01.12]. Wisker, G. (2008) The Postgraduate Research Handbook. Hampshire: Palgrave Macmillan.

478

An International Approach to Creative Pedagogy and Students’ Preferences of Interactive Media Florica Tomos¹, Peter Mozelius², Olga Shabalina³, Oana Cristina Balan⁴, Christos Malliarakis⁵, Christopher Miller¹, David Turner¹ and Paul Jones⁶ ¹South Wales University, UK ²Stockholm University, Sweden ³Volgograd State Technical University, Russia ⁴Cardiff University, UK ⁵University of Macedonia, Thessaloniki, Greece ⁶Plymouth University, UK florica.tomos@southwales.ac.uk mozelius@dsv.su.se o.a.shabalina@gmail.com oc.balan@live.com malliarakis@uom.gr cjmiller@southwales.ac.uk david.turner@southwales.ac.uk paul.jones@plymouth.ac.uk Abstract: The world population lives within an information society, depicted as an era of “integrated software applications” where the new technology and information technology paradigms are affecting the global environment and the international trend of education (Tapscott and Caston, 1993; Ottestad, 2010). Within this context e‐learning is presented by Ottestad (2010) as an emerging pedagogy where teachers’ creativity, competence and professionalism (Liakopolou, 2011; Davies, 2013) as a quality force come together with the new technologies to meet students’ preferences for optional learning resources, to empower students and increase their confidence as well as helping learning, understanding, reinforcing knowledge, stimulating interest, increasing collaboration and motivation (Craft and Jeffrey, 2008; Inglis et al., 2011). Previous studies demonstrated that the generation of learners called “Net generation”, “Millenians”, “Digital natives” or “Web generation” (Tapscott, 1998; Howe and Strauss, 2000; Prensky, 2006; Hartmann, 2003 in: Van den Beemt et al., 2011) uses the interactive media vigorously (Duimel and DeHaan, 2007; Schulmeister, 2008 in: Van den Beemt et al., 2011). This study is an international approach developed across four countries: Wales, Sweden, Russia and Greece. The research conducted focus groups based on e‐learning resource presentation, a survey with questionnaires and case studies. The results demonstrated students’ preferences for dynamic presentations, effective animation, comic effects (Kruger, 2004) and interactive media. In order to find different learning styles and preferences, online and paper form Questionnaires for course evaluation and assessment were conducted and compared with teachers’ observations and notes. The study provided optional as well as additional e‐learning resources in order to reinforce student learning and it was based on learning theories such as: the concept of learning and reinforcement, remembering and schema which can activate experiences stored in the mind (Skinner, 1930 in Kintsch, 1977; Bartlett, 1995). The results of this research support the constructivist view of cognitive development theory, the role of visual and the practical knowledge (Vygotsky, 1978; Piaget, 1970; Wadsworth, 1979). Keywords: e‐learning, creative pedagogy, preferences, net generation, reinforcement

1. Introduction The use of innovative technological tools for learning has become part of students’ approach to learning. Researchers have examined the role of e‐learning in the net generation’s learning styles and emerging pedagogy (Zu et al., 2010; McGill and Hobbs, 2008; Van den Beemt et al., 2011; Jones and Czerniewicz, 2010; Ottestad, 2010). The scope of the research will be to find out the impact of creative pedagogy and novelty electronic resources on students’ satisfaction and preferences. To identify students’ learning preferences, the team of researchers conducted an extended analysis on undergraduate and postgraduate students within different faculties, across four countries and over the impact of using different electronic resources: Moodle, Blackboard, e‐learning teaching and revision guide and employed focus groups and survey with questionnaires, interview, teachers’ observation notes and case studies.

479

Florica Tomos et al.

2. E‐learning – emerging pedagogy and teachers’ creativity There have been two recent changes in the global environment: the shift to multiform user interfaces and the shift to integrated software applications. These are combined with network computing, meaning that the users are now able to access massive amounts of information and applications and stay interconnected, wherever and whenever they choose (Tapscott and Caston, 1993). As a result, a world of sharing has opened up and collaboration across social, cultural, political, economical and educational systems (Tapscott and Williams, 2007) to the extent of creating collaborative communities, described as “long term partnerships” distinguished by “interaction, communication, information sharing and trust” (Alder and Heckscher, 2006, p. 363). The new generation of students is called the “Net generation” (Tapscott, 1998, in: Beemt, et al., 2011), “Millenials” (Howe and Strauss, 2000, in: Beemt et al., 2011), “Digital natives” (Prensky, 2006, in: Beemt et al., 2011) or “Web generation” (Hartmenn, 2003, in: Beemt, et al., 2011). Although some authors (Bennett et al., 2008, in: Beemt et al., 2011) claim high information and communication abilities are the main characteristics of this generation, other authors found that, the students belonging to this generation use interactive media vigorously (Duimel and DeHaan, 2007; Schulmeister, 2008, in: Beemt et al., 2011), but students’ information communication abilities were moderate (Cameron, 2005; Margaryan and Littlejohn, 2008, in Beemt et al., 2011). Moreover, research finds a strong relationship between the net generation, interactive media and education (Oblinger and Oblinger, 2005, Shaffer and Gee, 2005 in: Beemt, et al., 2011). Jones and Czerniewicz (2010) stress the natural abilities of the net generation with regard to the new technologies. Bennett et al. (2008, in: Jones and Czerniewicz, 2010) suggested that this generation has specific preferences for learning styles, while Prensky (2001b, in: Jones and Czerniewicz, 2010) claimed that the brain changes as a result of the long interaction with technology. With reference to these changes, Ottestad (2010) highlighted that the main characteristic of pedagogical practice was the appeal for innovative teachers, capable of shifting from the traditional teaching practice towards the new emerging pedagogy. A research study conducted by Voogt and Plomp (2010) demonstrated the need to embed information communication technology as a lifelong learning element for professionals involved with teaching. Furthermore, Nicholl and McLellan (2008) emphasised that students’ performance is the answer to teachers’ performativity. Accordingly, teaching requires competence, pedagogical skills, specialist knowledge and technology knowledge (Liakopolou, 2011; Zheng et al., 2008). Along with these, Kim et al. (2007) recommended as significant, within the learning and teaching process and within the global technological trend, the effective role of animation and interactive learning which are already embedded into learning materials.

3. Learning theories and students’ preferences According to Vygotsky (1978) the human intellectual developments as well as the incipient forms of intelligence both practical and abstract occur when action and communication intersect with each other. Consequently, it could be suggested that it is the social interaction and the action that determine learning (Vygotsky, 1978). Additionally, Piaget (1970) argued about the theoretical connection between “internalization” and “externalization” as pre‐conditions for the learning process. Explaining the constructivist view on learning, Fox (n. d. p. 10) stated that constructivism “has been used in relation to theories of perceptions, of memory and of learning”. Additionally, Bartlett (Fox, n. d., p. 10) described memory as a “process of reconstruction from stored traces of experience”. Nonaka highlighted the relationship between knowledge creation and sharing knowledge and suggested that sharing knowledge (explicit and tacit knowledge) is the foundation of knowledge creation (1994, in: Kwok, et al., 2002). Finally, do students have certain preferences with regard to effective interactive media and comic effects (Kruger, 2004)? According to Kruger (2004) it appears that teaching comics and incorporating visual comical portrayal it improves learning, meets students’ preferences and creates motivation. There is an abundance of e‐learning resources accessible online and students display preferences when choosing between them (ADL 2003; Tzikopoulos et al., 2007; Ochoa and Duval, 2009 in: Manouselis et al., 2010). According to Manouselis et al. (2010) the reason students prefer to use interactive media is sharing, collaboration and community construction. Within the new context of technological revolution both information technology and education professionals are developing innovative e‐learning resources in order to assist the new “learning paradigm” (Anafnostopoulos and Bielikova, 2010). A pilot study conducted by Van den Beemt et al. (2011) demonstrated the existence of different preference for learning styles.

480

Florica Tomos et al.

4. Research methodology The aim of this research was to explore students learning preferences when accessing online learning resources and to answer the following research question in order to meet students’ needs: “Does teachers’ professionalism and competence in creating additional electronic resources with new technologies empower students’ learning and meet students’ needs?” In order to find students’ preferences and satisfaction, similar research was conducted in the UK, Sweden, Russia and Greece by employing a multi‐methods approach (Saunders et al., 2009), both quantitative and qualitative methods: Focus Group, Case Study, Survey, Interviews, Observations, Teacher’s Notes and Forum Discussion. The research used non‐probability sampling techniques and questionnaires as research instruments for data collection. Finally, questionnaires were designed to include both open and closed questions (Saunders et al., 2009).

4.1 Focus group in the United Kingdom – findings and analysis In order to address the research question, the study considered a focus group with both undergraduate and postgraduate students from different courses, as well as teachers’ observations and notes. In the first phase, the students were asked to see two PowerPoint presentations of e‐learning resources. The employed method was a qualitative method, offering the participants a chance not only to witness each other viewpoint, but also interacting and agreeing with other students’ opinion (Bryman and Bell, 2011). The first electronic resource was called “Revision Guide in Accounting”; the second electronic resource presented to the focus group was an additional e‐learning resource for the Business Research Course for undergraduate students. The focus group was composed of students from business and advanced technology faculties on undergraduate and postgraduate courses, both male and female and ages ranging from 19 to 30 years old. In the second phase of the focus group, each member was given a list with questions and the mediator interviewed each member of the group in order to find out their opinions and preferences regarding the e‐learning resources and interactive media presented to them. The researcher took notes and recorded the discussion, in order to keep a track of students’ viewpoints. In order to facilitate researcher’s triple role as moderator, taking notes and recording, the focus group was kept small with just three students (Bryman and Bell, 2011) using interviewing techniques to collect qualitative data (Saunders et al., 2009).

Figure 1: E‐learning resource – Menu Navigation Figure 2 – E‐learning ‐ fashion & textile design The findings of this research suggested that the majority of the respondents preferred both Blackboard and additional electronic resources. All the participants thought that the quality of design was high or very high in the quality of the e‐learning resources and ready to be used across different subjects. All the participants agreed with the 100% innovative contribution to teaching and learning. They thought that the learning resource was innovative, additional, new pedagogical approach to teaching and learning, helping students with effective learning and they preferred dynamic presentations. Question 4: Do you think that by using e‐learning resources students will increase: interaction, learning, participation and knowledge?

481

Florica Tomos et al. Table 1: E‐learning resource – question 4 Answers

Students (%)

Interaction

Yes 66

Not sure 33

Learning Participation Knowledge

100 100 100

‐ ‐ ‐

The third e‐learning resource was created by a PGCE Teacher Trainee in Higher Education and piloted with both students on BTEC National Diploma and undergraduate students on Foundation studies in Fashion Design and Textiles in the United Kingdom. The findings were based on observations and notes taken by the Teacher Trainee. According to these, students preferred animation and interactivity together with innovative and creative learning resources. Beside this, a positive feedback from students was given on the humoristic effects on comic characters and celebrities inspired from the contemporary media.

Figure 3: The aim of the module

4.2 Case study in Sweden – findings & analysis The overall approach for the Swedish part is a case study strategy defined as an empirical inquiry investigating real world phenomena (Yin, 1989). A case study is also an approach where researchers explore and evaluate processes or activities in depth using a mix of data collection methods (Creswell, 2009) with the idea of combining different data sources to generate a deeper understanding of the analysed phenomena (Remenyi, 2012). Data has been collected by group discussions, informal interviews, in an online questionnaire and from online discussion fora. This case study is based on the course ID:WEBPROG that is a mandatory programming course for students taking Computer Science programmes specialised in interaction design and IT for market communication. The course is given in blended mode with traditional lectures and teaching sessions combined with recorded material and online activities provided in the virtual learning environment Moodle. About 100 students are taking the course in the 2013 spring semester and some of them in distance mode only. All assignments can be found and submitted online in the Moodle platform where the students submitted solutions also will be corrected and given feedback. Facilitation is given traditionally in face‐to‐face sessions in computer halls but also online in the Moodle environment. ID:WEBPROG has a focus on object‐orientation and web programming where the students uses Python, JavaScript, HTML5, Cascading Style Sheets (CSS), jQuery and the Django framework to solve a mix of mandatory and voluntary assignments. The overall course objective is to increase the knowledge about the construction of object‐oriented and web‐ based interactive systems. All assessment is based on programming assignments and there is no written exam. Everything can be completed online by distance but with traditional teaching and learning sessions available in a dual mode setup. There are large variations in the students pre‐knowledge but all students have completed an introductory programming course. At the end of the course students have to attend an examination seminar where their solutions to the assignments should be presented and the course outline is discussed and evaluated. Some of the discussions were initiated with the aim to provide data for this study and extra questions were added to the course evaluation questionnaire. In one of the mandatory assignments the students construct their own e‐Portfolios where the solutions of the assignments are described and commented to support reflection and a deeper understanding of the used techniques. The e‐Portfolios should

482

Florica Tomos et al. also include a presentation of the student and a CV with the idea that the online e‐Portfolios later could be linked in job or research applications. In these portfolios the students are also supposed to give comments on pros and cons of the given course assignments. Findings indicate that different students have different learning preferences. Some students definitely prefer to work by distance in online mode with the possibility of a flexible schedule and with the idea of anytime anywhere. Others find it valuable to meet other students and teachers face‐to‐face when they get stuck in the solving of the course assignments. The major part of this student group can definitely be classified as a Web generation but hardly as Digital natives in the sense that they are used to consume digital content but that it is only a minor part of the batch that have earlier experience of producing web content. The pre‐knowledge from Swedish secondary school shows great variations between different schools and secondary school programmes. Students’ prerequisites seem to have an impact on the attitude towards e‐learning as well where students with earlier experience of programming and online platforms have a stronger tendency to work online than students that come from secondary school programmes without technology enhanced learning. Students in this course batch are more or less all from the age group that can be named Generation Y (Mozelius, 2012) where earlier research has claimed that Generation Y students are less skilled when it comes to reading texts and a preference for visual information. Answers to questions in the course evaluation also shows that recorded lectures are appreciated both by students that have attended as well as being absent when the same lecture was given face‐to‐face. There is also positive feedback from the students when it comes to recorded tutorials and that visual step‐by‐step tutorials are more popular than text based ones. Interviewed students also claims that they prefer to build digital artefacts as examination compared to traditional written exams. The most popular assignments are the open ones that involve design with HTML5, CSS and jQuery. More traditional and closed programming exercises where given algorithms or class hierarchies should be implemented seem to be less appreciated.

4.3 Survey in Russia – finding and analysis Two main e‐learning technologies are used at VSTU for teaching students:

for giving lectures teachers use multimedia classrooms and present lecture materials in a form of Power Point presentations (Figure 4,a);

courses materials including lectures, presentations, tutorials, tests and questions are placed on the site “VSTU Open Education Center ” (Figure 4,b) [edu.vstu.ru] based on the Moodle and are used by students for self‐learning and training and teachers for managing and monitoring learning process.

Example of Power Point presentation of the course “Computer‐Aided management”

b) VSTU Open Education Center (main page)

Figure 4: Organization of e‐learning at VSTU For exploring student attitudes to using e‐learning techniques a survey has been conducted. The focus group was composed of 23 undergraduate students from Computer‐aided Design Department, 19‐20 years old both genders. All the respondents have already attended and acquired experience of using e‐resources provided by the Open Education Center. They have also attended a certain number of lectures based on using Power Point presentations.

483

Florica Tomos et al. The aim of the survey was to identify students’ preferences and to collect their suggestions of how to improve the structure and the content of e‐resources. The majority of the respondents preferred both the Moodle and multimedia lecture presentations. The most frequent suggestion on how to improve lecture presentations was to make them more colourful and to include animated descriptions of some problems that are difficult to understand.

4.4 Case study/interview in Greece – findings and analysis The approach followed by the Greek researcher is similar to that described in the Swedish section. More specifically, the case study model was employed in order to monitor the process and gather findings during and after the case study's completion using various methods (Creswell, 2009). In our case, the evaluation methods comprised a group discussions, informal interviews and interim observation notes taken by the teachers during the study. All the above information was gathered and analyzed in order to provide a refined and meaningful contribution (Remenyi, 2012) to the research question of how students interact when they play games to learn and to what extent does this enhance their learning. The case study is based on a course named "Introduction to Computer Programming" and it is taught to students in secondary education. The teaching method of this course includes either writing lines of code in professional educational programming environments or applying an interactive environment with rich and attractive graphics. A few representative examples of the latter category are the Scratch (Resnick, 2007), Alice 3D (Cooper et al., 2003) and Greenfoot environments (Kölling, 2010). Scratch was developed by MIT Media Lab in 2007 and aims to introduce students in computer programming by allowing them to create their own games and also includes existing storylines that students can incorporate, using drag and drop commands. The extensive online community that has been assembled enables students to upload their Scratch programs, comment on others' and communicate amongst them, thus encouraging interaction during and after courses. The aim of Scratch is depicted in Figure 5. The underpinning of students’ imagination is, as shown, essential and strongly connected with the playing process of creating games. Moreover, in Scratch students can share their creations, and thus communicate and interact with each other towards exchanging knowledge and practice as well as reflect on learning through playing.

Figure 5: Aim of the MIT Media Lab (Resnick, 2007) The Greenfoot and Alice 3D environments follow the same philosophy as Scratch, but they focus on teaching object‐oriented programming. In our case study, we employed all three environments in a secondary education course with fifteen students. Students were divided into groups of three and they were required to program their own games, covering the educational material of each unit of learning by practicing and interacting with the environments. Overall, each group created eight games in an academic semester and later on evaluated the games of the other teams. The observation notes that the teachers drafted during the study indicate that students could easily understand each interface and how to use the modules of all three tools. The majority of the students showed a great interest in achieving the assigned goal by utilizing and enhancing their creativity. During the group discussions as well as the informal interviews, students seemed to express a positive attitude towards the possibility of future use of such programs in the classroom. More specifically, they mentioned that their ability to interact with the environment’s functions (e.g. navigate characters, drag & drop commands, view/hide explanatory messages etc) gave them stronger motivation to complete their tasks because they felt they were actively contributing to the solving of a given problem. Finally, as findings also showed in the Swedish case study, students with existing experience in e‐learning platforms/environments/games seem to have a greater understanding of how to work with such programs in comparison to students that are not familiar with

484

Florica Tomos et al. technology enhanced learning techniques. The latter category shows some difficulty in feeling comfortable with the new technologies and requires the teachers’ assistance during the majority of the study in order to achieve their goals.

5. Conclusion and recommendations Based on the results from the above studies conducted in the UK, Sweden, Russia and Greece and on the constructivist view of cognitive development theory, regarding the role of visual and the practical knowledge (Vygotsky, 1978; Piaget, 1970; Wadsworth, 1979) this research suggests that students have different learning preferences and they prefer both the traditional and a more flexible approach to learning. Furthermore, the research found that students preferred both PowerPoint Presentations with animated descriptions and the course materials with lecturers, tutorials and tests. From the results of Focus Group, observations and Teacher Notes, the study discovered that students prefer animation, interactivity and creative e‐learning resources. There were also similarities between the results of studies from Sweden and Greece regarding the impact of e‐ learning and visual information on students’ learning and satisfaction. According to the findings from the studies conducted in Russia and Sweden, a particular focus was place on the opinion from students belonging to the Net Generation and on their needs to engage within the learning and practical activities. In conclusion, there are limits created by the small sample size of the research and the results cannot be recommended for generalisation at this stage. Nevertheless, these simple comparative approaches could be considered for generalisation to more complex situations. Moreover, the results are statistically significant and indicative, illustrating a range of views within the student population across four countries. Therefore, further research is recommended with larger representative samples. Further research could identify which features of the e‐ learning environment are most effective for students, and how similar approaches could be employed more widely. The study is a contribution to knowledge and has implications for researchers, practitioners and course organisers. Finally, the research suggests the following recommendations:

A selection of course material that encourages students’ interaction and creativity seems to be a successful concept. This is in line with the main precepts of the constructivist approach to learning, and the new media would appear to provide benefits in developing learning and teaching methods which are effective, and which appeal to the interests of the students.

Different students have different learning styles and for the new net generation or digital natives there is an increased need for multi‐modal content in online learning environment.

Virtual learning platforms and the plethora of new digital teaching tools are seldom self‐explanatory and students as well teachers must get appropriate introduction to support efficient learning and teaching.

Increase students’ satisfaction by meeting their different learning styles and needs within HE institutes in different parts of the world and consider teachers’ creativity with e‐learning resources in supporting students’ preferences and needs.

References Adler, P.S. and Heckscher, C. (2006) Towards collaborative community. In: Adler, P.S. and Heckscher, C. eds. The Firm as a Collaborative Community. Oxford: Oxford University Press. pp 11‐106. Anafnostopoulos, I. and Bielikova, R.M. (2010) Editorial. “Adaptive technologies and methods in e/m‐Learning and Internet –based education”, Journal of Computer Assisted Learning, Vol 26, No. 4, pp 226‐226. nd Bartlett, F.C. (1995) Remembering, 2 ed. Cambridge: The Press Syndicate of University of Cambridge. Van den Beemt, A., Akkerman, R. and Simons, P.R.J. (2011) “Patterns of interactive media use among contemporary youth”, Journal of Computer Assisted Learning. Vol 27, No. 2, pp 103‐119. Bremen, C. (2012) “Enhancing e‐learning quality through the application of AKUE procedure model”, Journal of Computer Assisted Learning. Vol 28, No. 1, pp 15‐27. Bryman, A. and Bell, E. (2011) Business Research Methods. 3rd ed. New York: Oxford University Press Inc. Budge, K. and Cowlishaw, K. (2012) “Student and teacher perceptions of learning and teaching: a case study”, Journal of Further and Higher Education, Vol 35, No. 4, pp 549‐567. Clouder, L., Oliver, M. and Tait, J. (2008) “Embedding CELTs in a performance‐oriented culture in higher education: reflections on findings creative space”, British Educational Research Journal, Vol 34, No. 5, pp 635‐650. Cohen, D. (1983) Piaget: Critique and Reassessment. Piaget’s theory of Intellectual Development. Kent: Croom Helm Ltd. Cooper, S., Dann, W., and Pausch, R. (2003). Teaching Objects‐first in Introductory Computer Science. ACM SIGCSE Bulletin 35(1), 191‐195. Craft, A. and Jeffrey, B. (2008) Special Issue: “Editorial: Creativity and performativity in teaching and learning: tensions, dilemmas, constraints, accommodations and synthesis”, British Educational Research Journal, Vol 34, No. 5, pp 577‐ 584.

485

Florica Tomos et al. Creswell, J. W. (2009) Research Design, Qualitative, Quantitative and Mixed Methods Approaches, Sage Publications Inc, ISBN: 978‐1‐4129‐6557‐6 Ehlers, U.‐D. and Hilera, J.R. (2012) “Special issue on quality in e‐learning”, Journal of Computer Assisted Learning. Vol 28, No. 1, pp 1‐3. Fisher, T., Denning, T., Higgins, C. and Loveless, A. (2012) “Teachers’ Knowing how to use technology: exploring a conceptual framework for purposeful learning activity”, The Curriculum Journal, Vol 23, No. 3, pp 307‐325. Flavell, J.H. (1977) Cognitive Development. New Jersey: Prentice‐Hall, Inc. Inglis, M., Palipana, A., Trenholm, S. and Ward, J. (2011) “Individual differences in students’ use of optional learning resources”, Journal of Computer Assisted Learning, Vol 27, No. 6, pp 490‐503. Jones, C. and Czerniewicz, L. (2010) Editorial: “Special Section: Net Generation”, Journal of Computer Assisted Learning, Vol 26, No. 5, pp 317‐320. Kim, S., Yoon, M., Wang, S, ‐M., Tversky, B. and Morrison, J.B. (2007) “The effect of animation on comprehension and interest”, Journal of Computer Assisted Learning, Vol 23, No. 3, pp 260‐271. nd Kintsch, W. (1977) Memory and Cognition. 2 ed. USA: John Wiley & Sons Inc. Kwok, RC‐W., Lee, J‐N., Huynh, M.Q. and Pi, S‐M. (2002) “Role of GSS on collaboration problem‐based learning: a study on knowledge externalization”, European Journal of Information Systems, Vol 11, No. 2, pp 98‐107. Kruger, E. (2004) “The use of the comics of T.O. Honiball in the Development of visual and cultural literacy”, Journal for Language Teaching, Vol 38, No. 1, pp 159‐171. Kölling, M. (2010). The greenfoot programming environment. Trans. Comput. Educ., 10:14:1‐‐14:21. Liakopoulou, M. (2011) “Teachers’ Pedagogical Competence as a Prerequisite for Entering the Profession”, European Journal of Education, Research, Development and Policy, Vol 46, No. 1, pp 474‐489. Liu, Z. and Roychoudhury, A. (2012) “Relating software validation to technology trends”, International Journal on Software Tools for Technology Transfer”, Vol 14, No. 6, pp 631‐639. Looney, J. (2011) “Developing High‐Quality Teachers: teacher evaluation for improvement”, European Journal of Education, Research, Development and Policy, Vol 46, No. 1, pp 440‐456. Manouselis, N., Vourikarit, R. and Van Asschet, F. (2010) “Collaborative recommendation of e‐learning resources: an experimental investigation”, Journal of Computer Assisted Learning, Vol 26, No. 4, pp 227‐243. Marshall, S. (2012) “Improving the quality of e‐learning lessons from e‐MM”, Journal of Computer Assisted Learning, Vol 28, No. 1, pp 65‐79. McCarthy, M. (2011) “Making every lesson outstanding: realistic classroom strategies for success”, Education Today, Vol 61, No. 3, pp 22‐25. McGill, T.J. and Hobbs, V.J. (2008) “How students and instructors using virtual learning environment perceive the fit between technology and task”, Journal of Computer Assisted Learning, Vol 24, No. 3, pp 191‐203. McPherson, M.A. and Nuneset, J.M. (2008) “Critical issues for e‐learning delivery: what may seem obvious is not always put into practice”, Journal of Computer Assisted Learning, Vol 24, No. 5, pp 433‐445. Mozelius, P. (2012) "The Gap between Generation Y and Lifelong Learners in Programming Courses – how to Bridge Between Different Learning Styles?", in proceedings of the EDEN 2012 conference, Porto, Portugal Mueller, J., Hutter, K., Fueller, J. and Matzler, K. (2011) “Virtual worlds as knowledge management”, Information Systems Journal, Vol 21, No. 6, pp 479‐503. Nicholl, B. and McLellan, R. (2008) “’We’re all in this game whether we like or not to get a number of As to Cs’, Design and technology teachers’ struggles to implement creativity and performativity policies”, British Educational Research Journal, Vol 34, No. 5, pp 585‐600. Oliver, M. (2011) “Technological determinism in educational technology research: some alternative ways of thinking about the relationship between learning and technology”, Journal of Computer Assisted Learning, Vol 27, No. 5, pp 373‐385. Ossiannilsson, E. and Landgren, L. (2012) “Quality in e‐learning – a conceptual framework based on experiences from three international benchmarking projects”, Journal of Computer Assisted Learning, Vol 28, No. 1, pp 42‐52. Ottestad, G. (2010) “Innovative pedagogical practice with ICT in three Nordic countries – differences and similarities”, Journal of Computer Assisted Learning, Vol 26, No. 6, pp 478‐492. Paine, L. and Zeichner, K. (2012) “The Local and the Global in Reforming Teaching and Teacher Education”, Comparative Education Review, Vol 56, No. 4, pp 569‐584. Piaget, J. (1970) The Principles of Genetic Epistemology, London: Routledge & Kegan Paul Ltd. Remenyi, D. (2012) Case Study Research, Academic Publishing International Limited, Reading, United Kingdom, ISBN: 978‐ 1‐908272‐40‐9 Resnick, M. (2007). All I Really Need to Know (About Creative Thinking) I Learned (By Studying How Children Learn) in Kindergarten. Proceedings of the SIGCHI Conference on Creativity and Cognition. Sahlberg, P. (2011) “The Fourth Way of Finland”, Journal of Educational Change, Vol 12, No. 2, pp 173‐187. Sarigöz, I.H. (2008) “Towards Individual Gendered Foreign Language Teaching”, Journal of Language and Linguistic Studies, Vol 4, No. 1, pp 51‐59. th Saunders, M., Lewis, P. and Thornhill, A. (2009) Research Methods for Business Studies. 5 ed. Essex: Pearson Education Limited. Shirley, D. (2011) “The Fourth Way of Technology and Change”, Journal of Educational Change, Vol 12, No. 2, pp 187‐209. Simmons, R. and Thompson, R. (2008) “Creativity and performativity: the case of Further Education”, British Educational Research Journal, Vol 34, No. 5, pp 601‐618.

486

Florica Tomos et al. Stanulis, R.N., Little, S. and Wibbens, E. (2012) “Intensive mentoring that contributes to change in beginning elementary teachers’ learning to lead classroom discussions”, Teacher and Teacher Education. International Journal of Research and Studies”, Vol 28, No. 1, pp 32‐34. Tapscott, D. and Caston, A. (1993) Paradigm Shift. The New Promise of Information Technology, USA: McGraw‐Hill Inc. Tapscott, D. and Williams, A.D. (2007) Wikinomics, The International Best Seller, “How Mass Collaboration Changes Everything”, GB: Atlantic Books. Vavrus, F. and Bartlett, L. (2012) “Comparative Pedagogies and Epistemological Diversity: Social and Materials Contexts of Teaching in Tanzania”, Comparative Education Review, Vol 56, No. 4, pp 634‐659. Voogt, J. and Plomp, T. (2010) Special Section: Pedagogical Use of ICT Worldwide, Editorial: “Innovative ICT – supported pedagogical practices: results from the international study of information technology in education”, Journal of Computer Assisted Learning, Vol 26, No. 6, pp 449‐453. Vygotsky, L.S. (1978) Mind in Society, Cambridge, Massachusetts: Harvard University Press. Zheng, R.Z., Yang, W., Garcia, D. and McCadden, E.P. (2008) “Effects of multimedia and schema induced analogical reasoning on science learning”, Journal of Computer Assisted Learning, Vol 24, No. 6, pp 474‐483. Zu, C., Valcke, M. and Shellens, S.T. (2010) “A cross‐cultural study of teacher‐perspectives on teacher roles and adoption of online collaborative learning in higher education”, Journal of Further and Higher Education, Vol 33, No. 2, pp 147‐165. Wadsworth, B.J. (1979) Piaget’s Theory of Cognitive Development, New York: Longman Inc. Yin, R. K. (1989 ‐ 2008) Case study research: Design and Methods, Thousand Oaks: Sage

487

The Influence of the “Approach gap” Between Students’ and Teachers’ e‐Learning Preferences Nazime Tuncay Ministry of Education, Nicosia, North Cyprus nazime.tuncay@gmail.com Abstract: It is essential for teachers to have emic knowledge for their intuitive and empathic understanding of students’ attitudes and perceptions towards e‐Learning. Students should have etic knowledge so that teachers are able to compare their own students’ e‐Learning preferences. An online questionnaire has been developed in North Cyprus to find the differences between students’ and teachers’ emic and etic approaches towards e‐Learning. The sub‐questions of this research study are: What are the differences between students’ emic approaches and teachers’ etic approaches to their e‐ Learning preferences? What are the differences between students’ etic approaches and teachers’ emic approaches to their e‐Learning preferences? 200 students and 50 teachers completed the “E‐Learning Preference” questionnaire, using a five point Likert scale. The questionnaire consisted of three parts: The first part consisted of “demographic questions”; the second part consisted of “emic questions”; and the third part consisted of “etic questions”. In the students’ questionnaire, emic items consisted of questions like “I prefer sending my homework via email instead of bringing it to my teacher as printouts” and etic items consisted of questions like “Teachers prefer taking our homework via email instead of collecting it as printouts”. On the other hand, the emic items in the teachers’ questionnaire consisted of questions like “I prefer collecting homework via email instead of bringing it as printouts” and etic items consist of questions like “Students prefer sending their homework via email instead of bringing it as printouts”. Teachers and students were expected to provide answers to the emic part of the questionnaire according to their own personal point of view. For the etic part of the questionnaire, they were expected to provide answers according to someone else’s view. There were many fascinating results in this study. One of them is the following: The majority of teachers preferred collecting homework as printouts; whereas students thought that they preferred e‐mails. A minority of students preferred learning on their own, via educational game programs, whereas the majority of teachers thought students preferred learning via collaborative educational games. Students who are not showing any objections to teachers do not mean that they like things that teachers do. Similarly, teachers’ e‐teaching approach does not represent their preferences. What does all this mean? Should we expect students and teachers to present an etic approach or an emic approach to e‐Learning usage in classrooms? Should teachers work collaboratively to develop strategies, methods, and teaching materials to help students think globally? Nonetheless, there is an Approach Gap between teachers' and students’ e‐Learning preferences. Discussions and recommendations about how to fill this gap are also included in the paper. Most of the studies delivered today are either emic or etic. However, to have a more complete view of the whole situation in e‐Learning research, both global and local thinking are necessary. This research study draws the attention of researchers to an entirely different point: “Approach Gap”. Keywords: approach gap,e‐learning, emic approach, etic approach, students, teachers

1. Introduction Especially in the last 10 years, e‐Learning has been the subject of many research articles. One can find several researchers with emic approaches; however, the researchers with etic approaches are extraordinarily limited. E‐Learning can provide better support for the less able, engage students who do not respond well to ‘traditional’ classroom learning, provide an opportunity for accelerated learning for gifted and talented students, and develop independent learning skills through a personalized learning experience (Boulton, 2008). Students’ and teachers’ e‐Learning preferences may vary according to their previous knowledge, culture and their “approach”. “Etic” and “emic” are two types of approaches that are widely used by researchers to describe a specific situation in a more global or local understanding. Although there are several approaches used to understand the students’ and teachers’ e‐education preferences, there is not any research that considers the “approach gap” among these findings. Nonetheless, the chain of learners, teachers, educators and specialists are widely affected by this multicultural, cross‐cultural, and intercultural e‐learning approaches. Learning style is a distinctive and habitual manner of acquiring knowledge, skills or attitudes through study or experience, while learning preference is favoring one particular mode of teaching over another (Sadler‐Smith, 1996). Similarly, every teacher has his or her own teaching style. However, remarkably few students and teachers have the experience of considering educational issues from different approaches. Selection of the learning path is considered as a recommendation for choosing and combining the sequences of learning objects according to learners’ preferences (Kurilovas, etc., 2013). That is, predicting students’ preferences is crucial.

488

Nazime Tuncay According to Matsumoto (1994), quantitative educational research typically adheres to one of the following two paradigms: (1) universalistic (etic) view, or (2) relativistic (emic) view. Many researchers automatically assume that their results are etic in nature when, in fact, practically all research is emic in nature (Gallagher, 2012). There are several researches presenting an emic approach; however, there are few researches with an etic approach and there are extremely limited researches presenting both perspectives. The etic approach has a tremendously famous place in psychological, sociological as well as educational researches. The emic approach investigates how local people think (Kottak, 2006). It is essential for the understanding of a culture, and it is essential for conducting effective researches. Technological innovations are not magic bullets, and will not provoke organizational change by just being developed (Gurteen, 2012). Etic researches fill the missing parts in the cultural perspective. The adoption of both etic and emic approaches facilitates a more detailed engagement with key constructs (McEntee‐Atalianis, 2011). Studying with etic approaches is not that hard once researchers are aware of the difference between the etic and emic approaches. A critically thoughtful e‐Learning community of practice requires collaborative agreement on goals, routines and activities; facilitators personally modeling critical thinking; facilitators developing/identifying and teaching the tools supporting a critically thoughtful community; participants shaping communicative interactions to encourage thinking (Balcaen, & Hirtzr, 2007). Many people find it hard to present an etic perspective; however, everyone has an emic perspective, which they use particularly frequently. Researchers present their findings with an etic view or with an emic view.. People should use the tools, technologies and platforms that they are familiar with and that best suit their capabilities and needs (Gurteen, 2012). The etic view considers research study findings to be truths that can be extended across all cultural groups or social groups. Presenting the etic view requires cultural competence and knowledge of the ways in which ethnic realities influence individuals. The emic view considers research study findings that reveal only particular truths in a single culture or social group. Researchers having the etic view are particularly important for deep analyzing of people’s inner speak. Cheung (2012), points out the advantages of a combined emic‐etic approach in bridging global and local human experiences in psychological science and practice. Technology is a fast changing issue. Sometimes it becomes terribly hard to follow new changes. Teachers and students belong to different generations; if they do not have an etic approach, they are more likely to have communication problems. The world is moving so fast that when it comes to Social Tools and Web 2.0, students know more than their teachers and parents (Gurteen, 2012). There are not any studies considering both etic perspectives and emic perspectives of teachers and students. This research study is delivered to fill the research gap in this area, as well as to draw attention to a new gap: “Approach Gap”.

1.1 Purpose The purpose of this research is to find the differences among teachers’ and students’ e‐Learning approaches. The sub‐questions of this research study are as follows: What are the differences among students’ emic approaches and students’ emic approaches to e‐Learning preferences? What are the differences among students’ etic approaches and students’ etic approaches on e‐Learning preferences?

2. Methods An online questionnaire has been developed in North Cyprus to find the differences among students and teachers emic and etic approaches towards e‐Learning. 200 students solved the student teachers “E‐Learning Preference” questionnaire and 50 teachers have solved the teachers “E‐Learning Preference” questionnaire, using a five point Likert scale. Both questionnaires consisted of three parts: The first part consisted of “demographic questions”; the second part consisted of “emic questions”; and the third part consisted of “etic questions”. The emic items in the students’ questionnaire consisted of questions like “I prefer sending my homework via email instead of bringing it to my teacher as printouts” and etic items consisted of questions like “Teachers prefer taking our homework via email instead of collecting it as printouts”. On the other hand, in the teachers’ questionnaire emic items consisted of questions like “I prefer collecting homework via email instead of bringing it as printouts” and etic items consisted of questions like “Students prefer sending their homework via email instead of bringing it as printouts”. Teachers and students were expected to give answers to the emic part of the questionnaire according to themselves. For the etic part of the questionnaire they were expected to give answers according to someone else’s viewpoint. Teachers were expected to give answers from the students’ perspective and students were expected to give answers from the teachers’ perspective.

489

Nazime Tuncay

3. Results and discussion Results are divided into five parts: “Etic Approach of Students’ and Teachers’ E‐Learning Preferences”, “Emic Approach of Students’ and Teachers’ E‐Learning Preferences”, “Difference Between Emic Approach and Etic Approach”, “Is this an Approach Gap?”, “What is the Influence of this Gap on Teachers and Students E‐ Learning Preferences?” In the tables “T” stands for Teacher and “S” stands for Student. The discussions about the results are also included in these parts.

3.1 Students e‐learning preferences and teachers e‐learning preferences (emic approach) The majority of students and teachers agree about learning via online educational games, writing on a mobile device instead of writing on a computer, and bringing technological devices to school. Table 1 shows the teachers and students emic approaches. 30% of the students agree that they prefer “preparing/reading” online course notes; however, 30% of the teachers disagree. Half of the students prefer bringing homework to school as printouts; 30% of them do not bother doing either. On the other hand, teachers have no preferences. There is an equal distribution among the teachers who like and who do not like collecting homework as emails. 40% of the students and teachers strongly like writing on computer. Half of the students and teachers strongly like studying at home. More students like joining online conversations and sending SMS than teachers. Half of the teachers do like joining one to one meetings. More teachers like joining online conversations than students. Half of the teachers prefer sending instant messages to sending SMS. However, students do not have any preferences. This may be due to students having the chance of sending SMS at very cheap prices. The majority of students prefer solving e‐questions; however, the majority of teachers prefer giving paper questions. These are notable differences among students and teachers emic approaches. Table 1: Teachers’ and students’ emic approaches Prefer preparing/reading online course notes sending email instead of bringing homework as printouts.

Strongly Agree

Neither Agree nor Disagree

Agree

Disagree

Strongly Disagree

20%

30%

20%

30%

20%

%10

10%

20%

30%

20%

30%

20%

%20

20%

40%

30%

10%

50%

20%

10%

40 (%)

20 (%)

30 (%)

10 (%)

20 (%)

10 (%)

20 (%)

10 (%)

10%

sending/receiving instant messages to SMS

50%

20%

10%

20%

10%

20%

10%

20%

solving e‐questions, rather than paper questions

10%

40%

30%

10%

learning via online educational games

50%

20%

10%

writing on a mobile device instead of writing on a computer

40%

30%

10%

bringing technological devices to school

50%

20%

10%

writing on computer instead of using notebook studying/teaching at home joining online conversations to joining one‐to‐one meetings

490

Nazime Tuncay

3.2 Students e‐learning preferences and teachers e‐learning preferences (etic approach) Table 2 shows the teachers’ and students’ etic approaches. Teachers have answered the questions as if they were students, and students have answered the questions as if they were teachers. A high percentage of the teachers think that students prefer “reading” online course notes; similarly a high percentage of the students think that teachers prefer “reading” online course notes! Teachers think that the majority of students do not prefer sending email, and they like bringing homework to school as printouts. On the other hand, the majority of students think that teachers prefer collecting homework as printouts. A high percentage of the teachers think that students prefer writing on the computer instead of writing in their notebooks. On the other hand, a high percentage of students think that teachers prefer using notebooks instead of using a computer. Table 2: Teachers’ and students’ etic approaches Prefer preparing/reading online course notes

Strongly Agree T 50%

S 40%

10%

Writing on computer instead of using notebook Studying/teaching at home

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

S 30%

T 20%

S 10%

T S 10% 10%

T 10%

S 10%

30%

T 30 (%) 30%

20%

30%

20%

20% 20%

10%

40%

10%

30%

10%

40%

10% 20%

10%

20%

50%

20%

10%

20%

10% 10%

10%

40%

10%

20%

30%

10%

30% 20%

20%

10%

Sending/receiving instant messages to SMS

60%

40%

20%

10%

20%

10%

20%

Solving e‐questions rather than paper questions

20%

40%

30%

20%

10%

20% 10%

10%

Learning via online educational games

10%

20%

10%

30%

40%

20%

20% 20%

20%

10%

Writing on a mobile device instead of writing on a computer

60%

20%

10%

30%

10%

30%

10% 10%

10%

Bringing technological devices to school

70%

60%

20%

10%

15%

10%

Sending email instead of bringing homework as printouts.

Joining online conversations to joining one‐to‐one meetings

15%

Above 60% of the teachers and students have said that they think students and teachers prefer studying / teaching at home. A high percentage of students think that students prefer teachers joining online conversations rather than joining one‐to‐one meetings. However, teachers do not think that students prefer one‐to‐one meetings. Above 70% of teachers think that students prefer “sending instant messages rather than sending SMS”; “writing on a mobile device instead of using a computer” and “bringing technological devices”.

3.3 Differences between emic approach and etic approach Although there are similarities, students’ Emic approaches show differences from teachers’ Etic approaches. 40% teachers have answered that students like reading online course notes; this percentage is 80% according to the students’ own answers. Teachers and students agree about "Writing on computer, instead of using a notebook" and "writing on a mobile device instead of writing on a computer”. When Figure 1 is explored, we see that we have a curved graph. This means that there are differences among students’ etic and teachers’ emic thoughts.

491

Nazime Tuncay

Figure 1: Student emic‐teacher etic graph The majority of students prefer sending teachers instant messages; whereas teachers think that students prefer sending instant messages. However, the majority of teachers, according to the research study, prefer writing offline questionnaires and according to them their students like sending e‐mails instead. Figure 1 shows the intersections between the students’ emic approach and the teachers’ etic approach. This shows that there are some points where students and teachers present the same approaches. Figure 2 shows another intersection (intersection 3). Students’ etic thoughts and teachers’ emic thoughts show similarities about the issue “bringing technological devices to schools”.

Figure 2: Student etic‐teacher emic graph When Figure 2 is explored, we see that we have a curved graph. This means that there are differences among students etic and teachers’ emic thoughts.

492

Nazime Tuncay

3.4 Is this an approach gap? According to this research study there is a gap between teachers’ etic approaches and students’ emic approaches. On the other hand, the gap between teachers’ emic approaches and students’ emic approaches is so narrow that it can be omitted. This can be interpreted as student participants of this research study being more successful than teacher participants in presenting an etic view.

Figure 3: Approach gap This is a significant result. If teachers do not have a successful etic approach, how are they to be expected to deliver courses to students coming from different walks of life? People always tend to look to problems from one point of view, but for better solutions both etic and emic approaches are necessary. This approach gap is illustrated in Figure 3. The approach gap in this study is defined as the meaningful difference between students defining their own preferences and teachers defining students’ preferences.

3.5 What is the influence of this gap on teacher’s and students’ e‐learning preferences? In this research study, an approach gap was observed between “Teachers’ etic approach” and “Students’ emic approach”. In correspondence to this, students’ e‐Learning preferences were observed. Teachers’ etic perspectives of students’ choices and students’ emic perspectives of their own choices were explored. Figure 4 shows “students’ e‐Learning choices”.

Figure 4: Influence of gap

493

Nazime Tuncay The blue columns show the teachers’ etic perspectives of students’ choices and the other columns show the student’s emic perspectives of their choices. In other words, the first columns show; what teachers think about what “students think” and the second columns show what “students do think”. It was seen that students, and teachers having different approaches resulted in their having different preferences! More teachers think that students prefer asynchronous education than actually students do. The percentages of students’ answers and teachers’ answers about synchronous education were similar. On the other hand, the number of teachers who said that students prefer asynchronous education was twice the number the students who actually said that they had preferred it. The numbers of students who preferred blended education was triple the number of teachers who thought the students preferred it. The influence of this gap is seen in Figure 4 and is interpreted by means of the difference among students’ and teachers’ asynchronous, synchronous and blended e‐Learning preferences. Is this all? Does the approach gap affect only e‐Learning preferences? What other side‐effects does this gap have? These are all open to discussion. Does this approach gap affect teachers’ success in teaching? Does it affect students’ success in learning? Similarly, the way you look at these situations may affect students’ and teachers’ motivation in education.

4. Conclusion and recommendations “Etic” and “emic” approaches are used to describe e‐Learning preferences. A research study is delivered in which students and teachers have presented their own etic and emic perspectives. There were differences among students’ emic and teachers’ etic approaches; also there were differences among teachers’ emic and students’ etic approaches. In the statistical analysis; an approach gap is found between teachers’ etic approaches and students’ emic approaches. What is more, the influence of this gap is seen on the students’ e‐ Learning choices. More research should be undertaken in order to put stress on this approach gap. Students, teachers and headmasters should be encouraged to present different perspectives in order to be able to understand each other. Three steps to bridge the approach gap can be: To draw attention to “different approaches” by encouraging studies in this area; To deliver seminars to teachers about the importance of their having etic and emic approaches; Teachers helping students to develop their problem‐solving skills using emic and etic approaches.

References Balcaen, P. L. and Hirtz, J., R. (2007). Developing Critically Thoughtful e‐Learning Communities of Practice, Electronic Journal e‐Learning, Vol. 5, No 3, pp. 173‐182. Booth, M. Z. (1999). Swazi Concepts of Intelligence: The Universal vs. the Local. Boulton, H. (2008). Managing e‐Learning: What are the Real Implications for Schools? Electronic Journal e‐Learning, Vol. 6(1), pp. 11‐18. Cheung, F. (2012). Mainstreaming Culture in Psychology, American Psychologist, Vol. 67, No.8, pp. 721‐730. Gallagher, J. J. (2012). A Distinction between Emic Research and Etic Research, Gifted and Talented International, Vol. 27, No.1, pp. 71‐72. Gurteen, D. (Ed.) (2012). Leading Issues in Social Knowledge Management. Academic Publishing International Limited, United Kingdom. Matsumoto, D. (1994). Cultural influences on research methods and statistics. Pacific Grove, CA: Brooks/Cole. McEntee‐Atalianis, L. J. (2011). The Value of Adopting Multiple Approaches and Methodologies in the Investigation of Ethno linguistic Vitality, Journal of Multilingual and Multicultural Development, Vol. 32, No 2, pp. 151‐167. Kurilovas, E., Zilinskiene, I. & Dagiene, V. (2013). Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach Original Research Article, Computers in Human Behavior, In Press, Corrected Proof, Available online 2 August 2013. Kottak, C. (2006). Mirror for Humanity, McGraw‐Hill, New York Lett, J. (2012). Emic / Etic Distinctions. Professor James Lett's Faculty Webpage. Retrieved from: http://faculty.ircc.cc.fl.us/faculty/jlett. Retrieval Date: 01.05.2012. Sadler‐Smith, E. (1996). Learning Styles and instructional Design. Innovations in Education and Training International, Vol. 33, pp. 185‐193

494

Tutoring and Automatic Evaluation of Logic Proofs Karel Vaculík, Lubomír Popelínský, Eva Mráková and Juraj Jurčo Knowledge Discovery Lab, Faculty of Informatics, Masaryk University, Brno, Czech Republic 256512@mail.muni.cz popel@fi.muni.cz glum@fi.muni.cz 173001@mail.muni.cz Abstract: Tutoring of logic proofs is an important part of undergraduate courses of logic. Commonly, a tutor trains and tests students’ skills to build correct logic proofs. We introduce a system for training of students’ ability to construct correct proofs in propositional or predicate logic. In addition to common techniques including presentations supported by slides and exercises we use animations which are based on carefully selected demonstrative examples and their step‐by‐ step solutions. Animations are interactive so that a student may choose a particular step, a sequence of steps, and/or a particular task. In order to test students’ knowledge, we prepared a questionnaire that captures the entire process of a logic proof construction. A student constructs a proof and then answers questions from the questionnaire. We describe the design of the questionnaire and discuss its dis/advantages. We then apply frequent subgraph mining together with supervised machine learning algorithms to perform an automatic evaluation of correctness of the proofs. In addition to classifying the proofs as correct or incorrect, a report containing the summary of errors and suggested penalty points is produced. Keywords: graph mining, logic proofs, resolution, automatic evaluation, frequent subgraphs, classification

1. Introduction Teaching constructive tasks, i.e. tasks that a student has to build in several steps – like tasks in descriptive geometry or logic and math proofs – implies a need of different evaluation techniques. E.g. in the case of resolution proofs, it is not sufficient to assign points based solely on the conclusion that the student reached. In Figure 1, the student reached the correct conclusion after an incorrect application of the resolution rule. That is why we need to verify that the sequence of steps – in this case a sequence of resolution rule applications – is correct.

Figure 1: Incorrect resolution proof

495

Karel Vaculík et al.

2. Teaching computational logic at FI MU 2.1 Courses Teaching computational logic at the Faculty of Informatics involves two courses, Introduction to Logic at the bachelor’s level and Computational Logic at the master’s. An important part of both courses is to teach students to build correct logical proofs, especially resolution and tableaux ones. The bachelor’s course Introduction to Logic is focused on propositional and first‐order predicate logic. It is attended by approximately 400 students annually and is taught by lectures and tutorials. To finish the course successfully, a student has to pass several homework online tests – 6 out of 9 in spring term 2013 – a midterm test and a midterm written exam. The final exam is again in the form of a test. The master’s course Computational Logic that is taught in English covers advanced parts of logic programming, automatic theorem provers, inductive inference, logics for natural language processing and semantic web and also non‐classical logics – modal, multi‐valued and fuzzy logic. Approximately 80 students attend it annually and both the teaching methods and requirements for passing the course are similar to the bachelor course.

2.2 Overview of the technology‐enhanced learning tools used In the process of building the courses as well as in tutoring and testing we use various TEL tools. In this paper we focus on those we created for teaching, practicing and testing of building logical proofs: animations, both non‐interactive and interactive, transformation of a proof construction into an online test, and a web‐based tool for practicing proof constructions. The last tool involves an agent for automatic evaluation of correctness of students’ solutions and their assessment. All these tools are helpful both for teachers and students; some of them even seem to be necessary because of the large number of students. In the following text, we briefly describe the role of animation in teaching computational logic at FI MU and introduce a questionnaire that has been developed for teaching logic proofs, namely resolution proofs. Then we focus on a tool for automatic classification of resolution proofs. We describe the system, the data used and experiments performed. In the concluding parts, we discuss possible improvements of the method and show future directions of this work.

3. Animations In building animations we followed the approach called short animated presentation (SAP). It is a simple and brief form of animation that focuses on just one topic of the presented task. The main goal of the use of SAPs here is to visualize the progress of a proof. Although the students are usually familiar with basic inference rules, they have difficulty in understanding the progression of a proof. With these animations they can go backward or forward in the proof and also – in case of the interactive animations – change the way of the proof and follow an alternative path. Usage of animations makes lectures more attractive and freely available animations make learning easier for students. Non‐interactive animations are based on carefully selected demonstrative examples and their step‐by‐step solutions. The series of steps are fixed and a user can only go forward and backward in the proof but cannot change the process of the animation. Every step is completed with an explanation and the relevant part of the construction is highlighted. All animations have been written in Flash. We have more than thirty SAPs that concern:

resolution and its refinements

tableaux proofs for propositional, predicate and modal logic

basics of inductive inference (version spaces, learning decision trees)

496

Karel Vaculík et al. Interactive animations are more suitable for students’ exercises and experiments. Users have the possibility to enter their own formulas to be proved and are able to control the process of an animation, e.g. select the steps of a proof. Currently we have interactive animations for tableaux proofs of propositional formulas. An example is illustrated in Figure 2.

Figure 2: Tableau proof in propositional logic

4. Proofs in online tests Online tests are used as a means for students to practice their knowledge (students can work with them repeatedly but the results do not influence the final grade) as well as for evaluating students. We use our university learning management system IS MU for building, maintaining and evaluating the tests (Popelínský, Mráková and Stehlík 2011). The main advantage of online tests is their quick automatic evaluation and assessment. However, the phase of their preparation is quite difficult and time consuming. Furthermore, isolated common test questions are not very suitable for constructive tasks like logic proofs. As we want to profit from the advantage of online tests, we suggest a method for transforming the task of constructing a logic proof into a set of test questions. Then, according to this method, we prepared tens of online tests concerning resolution and tableaux proofs. The main purpose of the transformation was to drive students to construct the proof first even though the construction itself would not be evaluated. Students should also understand the proof they built and be able to answer questions about it. An example of such test is shown in Figure 3. The most important features of our transformation method are the following:

The task requires construction of a proof very precisely.

Up to 20 yes‐no questions concerning the proof should be answered (selection of a subset is possible according to required difficulty).

We include questions which are difficult to answer without the construction of a proof.

Incorrect answers and missing correct answers are penalised to avoid guessing (instead of building a proof).

Questions are formulated very precisely.

We take into account that proofs are usually not unique despite the fact of precise formulation of the task.

497

Karel Vaculík et al. However, this way of testing has two drawbacks. It does not cover the dynamics (sequence) of building the tree and it is difficult for a teacher to create appropriate test questions. For these reasons, we decided to develop a tool that would enable a teacher to analyse all the steps of a logic proof. This tool that employs graph mining is described in the next section.

Figure 3: Example of test on resolution

5. Mining in resolution trees Educational data mining (Baker and Yacef 2009; Romero and Ventura 2010) is a challenging area that aims at educational data. In this chapter we present usage of data mining techniques for automatic evaluation of correctness of the proofs. Although the structure of resolution proofs is quite simple – it is based on an application of just one kind of inference rule – there is, to our knowledge, no tool for automatic evaluation of student solutions. The main reason may lie in the fact that building a proof is in essence a constructive task. It means that not only the result – whether the set of clauses is contradictory or not – but also the sequence of resolution steps is important for evaluating the correctness of a solution. In this paper we propose a method that employs graph mining (Cook and Holder 2006) for classification of the proof as correct or incorrect. For our purpose we used SLEUTH for its flexibility and good performance. SLEUTH (Zaki 2005) is an algorithm for mining all frequent subtrees in a database of labeled rooted trees. Input trees, either ordered or unordered, can be considered. It is also possible to search induced or embedded subtrees. In short, mapping between a tree and a subtree preserves parent‐child relationships in the case of induced subtrees and ancestor‐descendant relationships in the case of embedded subtrees. SLEUTH uses class‐ based extension mechanism for candidate generation. Classes are given by prefixes of trees. Since the algorithm handles unordered trees, extensions must be checked to determine whether they are in canonical forms. Finally, the frequency of subtrees is computed by using scope‐lists.

5.1 Data and data pre‐processing Data set contained 393 different resolution proofs for propositional calculus, 71 incorrect solutions and 322 correct ones. Each student solved and handwrote one task. To transform the students’ proofs into an electronic version we used GraphML (GraphML team 2007), which uses an XML‐based syntax and supports wide range of graphs including directed, undirected, mixed graphs, hypergraphs etc. We extracted following attributes from the proofs:

POINTS: Number of points the student obtained for their solution.

CLASS: Assigned class. We consider two possible values: TRUE, FALSE.

498

Karel Vaculík et al.

TRUE – The solution is a correct resolution proof (not necessarily of the type declared by the student).

FALSE – The solution is not a correct resolution proof of any type.

PARENTHESES: A type of parentheses used in the solution: curly brackets, square brackets, parentheses, none and unknown (unreadable).

RESOLUTION‐TYPE: The actual type of resolution used in the solution: F, <, S, LI, LD, SLD, Linear, none (incorrect solution), unknown (other type).

RESOLUTION‐STUDENT: Type of resolution declared by student: F, <, S, LI, LD, SLD, Linear, none.

SOLUTION‐ERRORS: List of errors in the solution.

The following errors can be registered: the actual type of resolution differs from the declared one, the proof was not finished or the result was not justified (when the requested proof does not exist), and usage of incorrect parentheses. The graph of the solution was also stored along with the above data. Every node had a unique id, a type (a type of the clause – Input or Derived), a clause, and a list of errors connected with the node. Common errors are the following: repetition of the same literal in the clause, resolving on two literals at the same time, incorrect resolution: the literal is missing in the resolved clause, resolving on the same literals (not on one positive and one negative), resolving within one clause, resolved literal is not removed, the clause is incorrectly copied, switching the order of literals in the clause, proof is not finished, resolving the clause and the negation of the second one (instead of the positive clause). For edges the following attributes were considered: id, a start node of the edge (a resolved clause), and an end node of the edge (a resolvent).

5.2 Finding characteristic subgraph for resolution proofs In the first experiment we looked for characteristic subgraphs for incorrect and correct resolution proofs. SLEUTH was performed on the whole set of data and then separately on the sets representing correct and incorrect proofs. One such subgraph is depicted in Figure 4. However, this method results in quite low accuracy. It was the reason why we decided to generate subgraphs that are frequent for a sufficiently large set of resolution proofs – so called frequent subgraphs – and use them as new features for classification.

Figure 4: A part of an incorrect tree

5.3 Frequent subgraphs for classification 5.3.1 Description of the method The system consists of five agents (Zhang et al. 2004; Kerber et al. 1995), or group of agents. For the purpose of building other systems for evaluation of graph tasks (like resolution proofs in different calculi, tableaux proofs etc.), all the agents have been designed to be as independent as possible.

499

Karel Vaculík et al. Agent A1 serves for extraction of a specified knowledge from the XML description of the student solution. It sends that information to agent A2 for detection of frequent subgraphs and for building generalized subgraphs. A2 starts with the maximum support and learns a set of frequent subgraphs which subsequently sends as a result to agent A3. Agent A3 serves for building a classifier that classifies the solution into two classes CORRECT and INCORRECT. In the step of evaluation of a student result there are two possible situations. In the case that a particular classifier has reached accuracy (or precision) higher than its threshold, agent A3 sends the result to agent A4 that collects and outputs the report on the student solution. In the case that a threshold has not been reached, messages are sent back to A2 demanding for completion of the set of frequent subgraphs, actually in decreasing minimum support and subsequently, learning new subgraphs. If the minimum support reaches the limit (see parameters), the system stops. We partially follow the solution introduced in (Zhang et al. 2004). The main advantage of this solution is its flexibility. The most important feature of the solution that is based on agents is interaction between agents in run‐time. As each agent has a strictly defined interface, some other agent of a similar function can replace it. New agents can be easily incorporated into the system. Likewise, introduction of a planner that would plan experiments will not cause any difficulties. In the following part, we focus on two main agents. The first one, agent A2, learns frequent patterns for a given minimum frequency, so called minimum support. The second one, agent A3, builds a classifier from 0/1 data where each attribute corresponds to a particular frequent subgraph and the attribute value is equal to 1 if the subgraph is present in the resolution tree and equal to 0 otherwise. A2 starts with a maximum support and learn a kind of frequent patterns, based on the parameter settings. As the tasks – resolution proofs – differ, there is a need for unified description of this kind of proofs. For that rea‐ son we use generalized resolution schemata, so called generalized frequent subgraphs. Each subgraph of a resolution proof is then an instance of one generalized frequent subgraph. More information can be found in (Vaculík 2013)Error! Reference source not found.. Then A3 builds a classifier from features generated by A2. In the case that the accuracy of classification by means of A2 is lower than the minimum accuracy demanded, a message is sent back to the frequent pattern generator. After decreasing the minimum support, the generator generates an extended set of patterns, or selects only emerging patterns (Dong and Li 1999). The system has been implemented mostly in Java and employs learning algorithms from Weka (Hall et al. 2009) and an implementation of SLEUTH. 5.3.2 Classification of correct and incorrect solution For testing of algorithm performance we employed 10‐fold cross validation. At the beginning, all student solutions were divided into 10 groups randomly. Then for each run, all frequent three‐node subgraphs in the learning set were generated and all generalizations of those subgraphs were computed and used as attributes both for the learning set and for the test set. The results below are averages for those 10 runs. We used four algorithms from Weka package, J48 decision tree learner, SMO Support Vector Machines, IB1 lazy learner and Naive Bayes classifier. We observed that the best results have been obtained for minimum support below 5% and that there were no significant differences between those low values of minimum support. The best results were reached for generalized resolution subgraphs that were generated from all frequent patterns, i.e. with minimum support equal to 0% found by Sleuth (Zaki 2005). The highest accuracy 97.2% was obtained with J48 and SMO. However, J48 outperformed SMO in precision for the class of incorrect solutions ‐ 98.8% with recall 85.7%. For the class of correct solutions and J48, precision reached 97.1% and recall 99.7%. The average number of attributes (generalized subgraphs) was 82.6. The resulting tree is in Figure 5.

500

Karel Vaculík et al. To increase precision on the class of incorrect proofs, we performed another experiment. We changed the classification paradigm to allow the classifier to leave some of the examples as unclassified. The main goal was to classify only those examples for which the classifier returned high certainty (or probability) of assigning a class. We used a validation set (1/3 of learning examples) for finding a threshold for minimal probability of classification that we accept. If the probability was lower, we assigned class UNKNOWN to such an example. Using 50 emerging patterns and threshold 0.6, we reached precision on the class of incorrect solutions 99.1% with recall 81.6% which corresponds to 73 examples out of 90. It means that 17 examples were not classified to any of those two classes, CORRECT and INCORRECT solution. Overall accuracy, precision and recall for the correct solutions were 96.7%, 96.2% and 99.4%, respectively.

Figure 5: Example of the decision tree

5.4 Report containing the summary of errors and suggested penalty points The main knowledge that is presented to the teacher is now two‐fold. He not only obtains the information of whether a given student solution is correct or incorrect but also the confidence level of that statement, i.e. the probability with that the classifier reached this result. Moreover, the results of this method can be further exploited for a more detailed report on an incorrect solution. From the decision tree in Figure 5 and an incorrect solution we can detect which path of the tree classifies the example. All the nodes on that path that express errors, e.g. resolution on two literals in one step, are added to the report so that the teacher obtains the explanation about the type and level of incorrectness.

501

Karel Vaculík et al. The solution proposed is independent of a particular resolution proof. We observed that only about 30% of incorrect solutions could be recognize with a simple full‐text search. In order to recognize the rest of them, we need a solution that employs more sophisticated analytical tools. We show that machine learning algorithms that use frequent subgraphs as Boolean features are sufficient for that task.

6. Conclusion and future work In this paper we described TEL tools suitable for tutoring and testing logic proofs, namely simple and brief animations, online tests based on the transformation of constructive tasks into a set of test questions, and a tool for an automatic evaluation of correctness of the proofs based on frequent subgraph mining and classification. We showed that with the use of machine learning algorithms – namely decision trees and Support Vector Machines – we can reach both accuracy and precision higher than 97%. We showed that precision can be even increased when small portion of examples was left unclassified. Currently, we have developed a web‐based tool for an input of a proof and also an agent for automatic evaluation of correctness of students’ solutions (Vrábel 2013). By means of this tool, the construction of a proof is monitored and all important actions are saved into a repository. For future work, we plan to use the results of the new system together with (Vrábel 2013) for printing a more detailed report about a particular student solution. It was observed, during the work on this project, that even knowledge that is uncertain can be useful for a teacher, and that such knowledge can be extracted from output of the learning algorithms. We also plan to extend a set of features with temporal patterns.

Acknowledgements This work has been supported by Faculty of Informatics, Masaryk University and the grant CZ.1.07/2.2.00/28.0209 Computer‐aided‐teaching for computational and constructional exercises. We thank to the members of KD Lab and Reshma Ramadurai for their help.

References Baker, R. and Yacef, K. (2009) “The State of Educational Data Mining in 2009: A Review and Future Visions”, Journal of Educational Data Mining, pp 3‐17. Cook, D. J. and Holder, L. B. (2006) Mining Graph Data, John Wiley & Sons. Dong, G. and Li, J. (1999) “Efficient Mining of Emerging Patterns: Discovering Trends and Differences”, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 43‐52. GraphML team (2007) “The GraphML File Format”, [online], http://grap hml.graphdrawing.org/ Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H. (2009) “The WEKA Data Mining Software: An Update”, SIGKDD Explorations, Vol. 11, No. 1. Kerber, R., Livezey, B. and Simoudis, E. (1995) “A hybrid System for Data Mining”, Intelligent Hybrid Systems, John Willey & Sons, pp 121‐142. Popelínský, L., Mráková, E. and Stehlík, M. (2011) “Teaching Computational Logic: Technology‐enhanced Learning and Animations”, Third International Congress on Tools for Teaching Logic, Salamanca. Romero, C. and Ventura, S. (2010) “Educational Data Mining: A Review of the State of the Art”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 40, No. 6, pp. 601‐618. Vaculík, K. et al. (2013) “Graph mining for automatic classification of logical proofs” (12th Czech‐Slovak conf. Znalosti 2013, accepted). Vrábel, P. (2013) “Web‐based tool for input and evaluation of resolution proofs”, Master thesis, Faculty of Informatics, Masaryk University, Brno. Zaki, M. J. (2005) “Efficiently Mining Frequent Embedded Unordered Trees”, Fundamenta Informaticae. Zhang, Z. and Zhang, C. (2004) Agent‐Based Hybrid Intelligent Systems, Lecture Notes in Computer Science, Vol. 2938, Springer Verlag.

502

The Global Classroom Video Conferencing Model and First Evaluations Charlotte Lærke Weitze1, 2, Rikke Ørngreen2 and Karin Levinsen2 1 VUC Storstrøm, Denmark 2 ResearchLab: IT and Learning Design, Aalborg University, Denmark cw@learning.aau.dk rior@learning.aau.dk Abstract: This paper presents and discusses findings about how students, teachers, and the organization experience a start‐up‐project applying video conferences between campus and home. This is new territory for adult learning centers. The paper discusses the transition to this eLearning form and discusses pedagogical innovativeness, including collaborative and technological issues. The research is based on the Global Classroom Model as it is implemented and used at an adult learning center in Denmark (VUC Storstrøm). VUC Storstrøms (VUC) Global Classroom Model is an approach to video conferencing and eLearning using campus‐based teaching combined with laptop solutions for students at home. After a couple of years of campus‐to‐campus video streaming, VUC started a fulltime day program in 2011 with the support of a hybrid campus and videoconference model. In this model the teachers and some of the students are present on campus in the classroom, while other students are participating simultaneously from their home using laptops. Although the Global Classroom Model is pedagogically flexible, the students are required to attend according to regulations from the Ministry of Children and Education to pass their exams. Evaluations show that the students are happy with the flexibility this model provides in their everyday life. However, our findings also show several obstacles. Firstly technical issues are at play, but also the learning design of the lessons, as well as general organizational and cultural issues. All these matters need to be taken into consideration when implementing the Global Classroom Model. Through the start‐up period of a PhD study and through a research‐based competence development project with senior researchers, we have gained knowledge about the experiences, challenges, and potentials of the teaching and learning within the Global Classroom Model. Both studies are action research studies with a user‐centered approach. In this paper we focus on the students experience and on the organizational issues related to the transition to the Global Classroom Model as well as on the continued development of the teachers’ educational designs. The research is based on interviews, on utterances in feedback sessions, and on the observed interaction taking place. Keywords: global classroom, video conferences, blended campus‐ and home‐based education, adult education

1. Introduction This paper presents findings about to how students, teachers, and the educational organization experience a video conference start‐up‐project, where students attend class on campus and from home synchronously. This is a new field for adult learning centers, and as our literature study shows, the specific Global Classroom model is in general a new kind of setup, that influences the pedagogic and learning design. Online‐education has developed into many shapes, both concerning the involved technologies as well as in the use of different pedagogies and learning designs (Laurillard 2012, Rice 2011). The variations of on‐line learning also involve the use of asynchronous or synchronous interaction in class. Videoconferencing is a technology that allows a more direct and immersive learning experience for the on‐line students since it enables a simultaneous face‐to–face interaction. Video‐conferencing has developed into two main forms in education: The parallel form, that uses dedicated video‐conference‐hardware has existed for many years and is for example used for reaching two‐three campuses or for reaching remote hubs of classes, international guest lectures etc. Whereas the laptop / web‐based form, that uses personal devices as PC’s or tablets and is a software‐solution has the freedom for the students to choose to sit separately at home or together on campus, using live‐streaming from everyone to everyone. (Andrews & Klease 1998, Freeman 1998, Kjær et al 2010, Roberts 2009). The two forms both has a major impact on the learning design as the first takes out‐set in the classroom and the teachers’ physical location herein, and the second uses a shared laptop space as the starting point of the activity. In the Global Classroom Model the two forms are merging. The teacher and students on campus use dedicated hardware solutions (Polycom Realpresence), but the students that are at home sign‐in to the classroom via a laptop‐software solution. Henceforth and unlike the literature describing technologies as Adobe Connect etc. this teaching process uses the classroom and the physical boards (whether a traditional chalk, whiteboard or a digital smart board) as reference point. The teacher both addresses the students in the physical classroom as

503

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen well the students online via representations on the projected screens (see later), this being two distinct modes of communication. We have only found little research describing on‐line education in this hybrid campus and home‐based form (as Ellingson & Notbohm 2012) and this is why we recommend further research in the area. In a review of the 50‐years history of the Australian Journal of Adult Learning (which is also an international journal), Harris and Morrison found that “The roles, characteristics and capabilities of educators have received decreasing interest” (Harris and Morrison (2011) p. 42) and they show (figure 8) that the number of papers on students are relative stable (average of 25%), but the papers on teachers fell from 1960‐2010 from 32% to a low 7%. Another trend (according to this journal) is a much higher (though fluctuating) degree of attention on evaluation than on curricula (Harris and Morrison (2001)). Little is known about how to enable teachers and the organization to establish curricula and designs for learning in video conference settings of, therefore intention is to use knowledge from students’ experiences to support the competence development process of the teachers. VUC (the case organization) is currently applying the global classroom model to the HF education. HF is a Higher Preparatory Examination Course (upper secondary general education program) that takes 2 years. To teach at HF requires “a Master degree in at least one relevant subject and to have completed a Post‐graduate teacher training course for upper secondary school teachers” (Milana 2008, p.7). However, it is a recent phenomenon that the majority of the teachers use technology in the teaching practice, such as sharing digital materials and using traditional learning management systems. The distributed video conferencing will furthermore make technology constantly present during the teaching. For the last 10 years, the Danish Government has focused on the implementation of IT in education, as a mean to increase the academic level and ensure that more people get an education. The argument is that IT provides better opportunities for differentiated and more flexible learning and evaluation forms (TDGME 2012). However, teachers lack an established practice and support (Riis 2012) when navigating in the many new opportunities within IT, whether it is by choice or as requirement of the job, hence, the need to return the focus to the teachers. At the same time, there is a need to examine what it takes to achieve a well‐functioning communication and decision‐making flow between the organization and teachers (Henriksen et al. 2011). Global Classroom uses video conference equipment that allows the teacher and students on campus to see and communicate synchronously with the students at home and vice versa. Every other week students can attend from home, and every other week they are obliged to go to the campus. The equipment in the class is situated in a way that enables 1) the teacher to see and hear the class at campus and at home at the same time, 2) the students to see the whiteboard in class and to see and hear the teacher in class and the students at home 3) the students at home to hear the classroom, to see the whiteboard as well as the teacher or the students at campus depending on which camera is used at campus (see figure 1). It is also possible to establish virtual‐group rooms for group‐assignments.

Figure 1: The global classroom set‐up

504

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen This is the ideal situation, but as this research study shows, situations occur where the students’ video is not on or there is trouble with the sound from the classroom and vice versa, all of which interrupts the planned designs for learning. However, our focus is primarily on the pedagogical possibilities when the system functions as intended, and technological discussions are primarily related to redesign of current setup to improve pedagogical processes. In the school year 2010/11 VUC had approx. 5,500 students (VUC 2013). HF‐Global Classroom represents a very small proportion of this (two classes respectively 10 and 26 participants (1.3.2013)). HF‐Global Classroom is VUC’s first initiative with a long‐term strategy in a relatively low population density area with long distances. One of the purposes is to ensure each citizen access to education regardless of time and place.

2. Research objective and methodology This is a joint research project between VUC and AAU. The overall research objective is to investigate: the design of innovative methods, practices and evaluation tools in relation to the use of IT in Global Classroom settings, with a focus on how to enable teachers to create motivating and qualified learning design for the students. The aim of the user‐centered action research is both to add value to VUC as well as to develop theory and guidelines by investigating how to qualify the implementation of the Global Classroom Model in general. The paper deals with the first two phases of the cyclic action research process, namely diagnosing and action planning (Susman & Evered 1978). Our understanding draws on the assumption that an innovative implementation of IT in formal learning situations takes place as an interaction between different actors, and that research of this kind needs to be grounded in mutual learning and dialogue. As such this is a participatory action research study. The sub‐questions for these particular phases become: Which innovative methods and practices are sustained or emerge when a team of teachers are “thrown” into a global classroom setup? What are the consequences for the students? How do the students perceive learning quality and motivation? Can any guidelines and/or future steps be derived from these first experiences? Given the nature of the research focus and questions it is vital that the empirical data provides insights that deepen the comprehensions and arguments behind the actions, consequently we have chosen qualitative methods in support of the action research study. The empirical material provides insight into the diagnoses and action‐planning phases as listed in table 1. Table 1: The material from the diagnose and action plan phases

3. Theoretical and grounded analysis of the empirical data Based on the three groups: students, teachers and organization (project‐ and pedagogical management, as well as technological personnel).

3.1 The students The Global Classroom Model consist of the video conference as a mediated learning process, and also comprises the use of other forms of IT in education including digital materials, software, and processes because of the changed environment for the learning design. For example, all the instructional materials should be accessible online (Rice 2011). In this way, the Global Classroom concept has inspired some of the

505

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen teachers to implement new kinds of IT in their teaching practice. These new ways of involving IT in the teaching may, together with the Global Classroom concept, potentially help to create a more relevant and motivating learning for the students (Somekh 2008). According to the German professor of pedagogy Thomas Ziehe there has been a "de‐conventionalisation" ‐ a change in young people's knowledge, behaviour, and motivation (Wiborg 2009). Today, young people are choosing what they want to learn, and young people's behaviour has changed because they have become major media consumers. The student’s motivation helps establishing interest in the subject matter and is therefore an important contributing factor to the learning process (Koster 2005; Weitze & Ørngreen 2012). The following three main driving forces underlie the motivation to learn and at the same time they cover the basic human psychological needs: 1) Curiosity, 2) The desire to achieve competence, and 3) Reciprocity: the desire to be an indispensable part of the community (Gärdenfors 2010). The argument is: if the learning is planned in a way that enables the student to achieve one or more of the three main motives, it will help the student to feel an inner motivation to learn (Gärdenfors 2010; Wiborg 2009). Motivational elements: In this study, the students explain that they find a number of aspects of the Global Classroom concept motivating. For example the students own choice of environment helps them manage their family and everyday life by not always having to be present at school. Several students are also pleased with being able to vary their classroom environment during a day by changing geographical location, and when sitting at home they have the feeling that the school‐day ended sooner. The format also creates a new "intermediate solution" for some, when they feel “sluggish” and normally would have taken a sick‐day. In this way, the concept contributes to their ability to complete their education. Technological‐pedagogical issues: The students experienced technical problems and many of these problems were solved along the way. Problems were partly due to Global Classroom being a new concept developed through a bottom‐up approach, and partly due to the fact that students and teachers, had to learn how to use the system from scratch. The Global Classroom seems to provide a transparent experience (Dourish 2001), giving the feeling that it is possible to simulate a traditional classroom. Therefore the teachers plan to apply various educational activities equivalent to what takes place in a traditional classroom to make the students at home engage in class conversation without causing noise in the class, and with the least possible delay in audio and photo. Because of the delay students often perceive it as disturbance when they speak. In addition, the human ear cannot filter sounds in the same way in online space as in physical space; all sounds are mixed and more difficult to differentiate (voices, moving of chairs, coughing etc.). Similarly, it has proved difficult to create groups across home and campus because of technological problems as well as issues with too much noise in the class. Pure home‐based‐groups also have problems in detecting when to “return” to the classroom debate. We see a need for the teachers to experiment with various ways of working actively across the constellations of home and campus. The students tell that they have been frustrated in relation to the communication with the technicians when something is wrong with the technology. Some problems are of so vital importance that the teacher or student should be able to get immediate technical assistance, as some types of technical breakdowns has the effect that the teaching cannot be carried out. Uncertainty about deadlines for repairs and corrective actions are inconvenient in everyday life and has concerned the students. Learning Design: The students’ experience that the teachers are very different in their approach when activating the students at home. Some teachers are very aware of home‐students asking them very directly to participate in the debate, while other teachers hardly pay any attention to the students at home. Some students find it difficult to make the teacher aware that they want to answer a question. This makes the students at home frustrated and uninvolved. Therefore, the students feel it is important for teachers to take this issue into consideration in the learning design and to be aware that the students at home would like to be invited more into the class activity. The students at home are using different strategies to solve this problem like writing to the campus‐students on Facebook. In our dialogues with the teachers we have also found that the class from August 12 who participated in the qualitative student evaluation is very different from the class from August 11 In the 2011‐class the students at home are always very active and also often the "diligent"

506

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen ones in the class. Consequently, it might not be the teachers that ignore the students at home, it may also be that students at home are less active and not so easy to activate. Another consequence of the Global Classroom setting is that it is important for the students to have access to all instructional material as well as assignments on‐line before the lesson begins. This gives the students a chance to participate actively in the current lesson by solving these assignments in spite of any technical difficulties that might arise. Rules in Global Classroom: The students are satisfied with the rules of conduct in Global Classroom regarding the recommendations on behaving as in a traditional class, e.g. not to attend in pyjamas from bed, no smoking etc. These rules have been developed bottom‐up as such situations did happen, and are changed regularly according to new experiences. One can, however, consider whether it would be beneficial to develop pedagogical recommendations on for instance: active participation, working in groups etc. Pedagogical Innovation: The students have been pleased with the learning designs that involved working on the Internet, as this gave equal opportunities for students at home and on campus, as e.g. preparing multimedia presentations. However, when inquiring about ideas for other initiatives the students had difficulties articulating new ideas. Thus so as for teachers it can be hard for students to think beyond the traditional educational culture. It is important to acknowledge that in spite of the many problems, in terms of technology, in relation to pedagogy, and mental stress issues the students’ perceive the video conference as advantageous and want to continue.

3.2 The teachers The teachers have not been employed specifically as Global Classroom teachers. Though they received initial training in the concept, it was, at first, difficult to imagine how it would be to work with. The IT‐pedagogical project group chose different approaches: short seminars, and later involving researchers conducting innovative workshops, but all the time also with a bottom‐up/ learning‐while‐doing approach. At times this was frustrating for the teachers, but considered necessary by the IT‐pedagogical project group, since this was new terrain. Somekh stresses that adopting to change is learning and, “like students, teachers need to learn actively and have opportunities to try things out and evaluate the outcomes on the basis of evidence, with the support of strong leadership and a community of peers” (Somekh 2008 p. 9). What sometimes is regarded as “teachers resisting to be innovative in their pedagogical practice” is indeed a complex and cross organizational issue, since teachers, students, managers, and project groups in the organization are all embedded in an educational culture that at the same time supports and restrains its members. Pedagogical innovation does not only concern and involve the teachers but the entire learning organization. Motivational elements: At the moment the teachers primarily regard Global Classroom as being beneficial for the students, and they appreciate that it makes it possible for some of the students to complete their education. The future development of the pedagogical aspects in the concept will hopefully also contribute to the teacher’s own motivational experiences within this frame. Pedagogical‐Technological issues: In the initial phase at VUC the teachers often had to spend a large part of their time and attention on making the videoconference technology work, experiencing that they wasted valuable teaching time. However, in our latest observations and interviews with the teachers, we note that several of the teachers tell that the technology now is running most days. Cognitive demands: The teachers experience sudden interruptions in the middle of a sentence in class, when students at the videoconference cannot see or hear the teacher clearly and therefore interferes out of the teaching context. Students use different strategies to solve this problem as writing to campus students on Facebook, since there are no chat facilities with the teacher. At the same time, the teachers experience mental overload due to the many media at play and the many points of attention. Many teachers experience an immense fatigue after a Global Classroom lesson. The student evaluation showed that it would be advantageous and less disruptive if the students used chat to submit information to the teacher during a lesson, but this is not necessarily the teacher’s desire. On the contrary, many teachers expressed reservations

507

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen about getting one more media to communicate in and keep an eye on, though a few forerunners seemed to have the energy to work with multiple media and students at 2 locations at the same time. Learning design and activity level: Just like the students, the teachers find it possible to carry out teaching in a traditional manner in the Global Classroom Model, and they see this as an advantage. There are communicative difficulties partly due to lack of the valuable flow and synergy that can be experienced in a traditional classroom discussion; due to sound delay and poor lighting; and due to some students that deliberately choose a passive role. Depending on where the most active students are, the "centre of gravity" in the activity level in the class or at home shifts. This is an interesting aspect in the debate since this highlights the importance of student engagement and study skills in general instead of only focusing on trouble with the technology. Facial decoding: Another problem occurs when the teacher cannot read students' facial expressions on the screen. Sometimes the teacher can only see the student's silhouette if he sits with the light coming from behind. One of the interviewed teachers did not want to humiliate the students that might be unable to answer. When in doubt it is generally much easier for the teacher to observe the facial expressions of the students. By reading facial expressions the teacher can evaluate whether the student does not know the answer, or if he's just shy and the teacher just needs to ask. "They are all adults, and the moment you ask them a question and they don’t respond; then I can’t see any point in going on." a teacher utters in an interview, with reference to the students’ having to take responsibility of their own learning process. Visual attendance: In Global Classroom it is a problem when a student at home "disappears" from the screen (the student leaves his laptop, turns of the web cam or logs‐off the system during a session). The students have to attend at least 80% of the lessons, and they can be expelled if they do not. When a student cannot be seen on the screen, some teachers choose to ignore it, others comment on it. At the student evaluation, some of the students expressed that the teachers were violating their trust if they commented harshly on how often they walked away from the screen. The students felt this only happened when they needed to, to go to the bathroom for example. The teachers, however, feel that they are facing a rather big responsibility evaluating whether the students are still participating in the lessons, and they frequently choose not to react when students disappear from the screen in order not to lose their trust, but they doubt whether this is the right strategy. This is an example of a stress‐creating issue that underlies the teaching and runs as an additional point of focus for the teacher during the teaching. Rules in Global Classroom: with respect to the issues of facial decoding and visual attendance, the organization could consider making additional rules. A simple solution concerning facial decoding would be to require that students at home have their face lit so the teacher is able to read their facial expressions. The teachers could also decide to discuss and agree on how common rules concerning visual (non)attendance should be. Pedagogical Innovation: Research shows that apart from a few enthusiasts, it is in general difficult for teachers to be innovative in their use of IT in the teaching. Teachers often settle for transferring their existing and inherent practice. This practice can certainly be really good, but according to the Danish Evaluation Institute teachers do not fully utilize the pedagogical and academic possibilities lying in front of them concerning the use of IT (EVA 2012). This indicates that teachers need to learn to work with IT learning tools, but also that they need support for the process of innovation and for the development of innovative thinking (Darsø 2011).

3.3 The organization Conversations and meetings with the organization's project owners has, along with the other empirical activities, illuminated classical issues in the change processes in which project managers at times are well ahead of the rest of the organization since they already understand the ideas within the process that they themselves have developed. This was evident in the SWOT analysis with the teachers, where the teachers articulated that they had a fundamental lack of insight into and influence on the process, as well as a frustration with the basic challenges in technology, pedagogy, and the organizational setup. This was in contrast to the project owners' first dissemination about the situation to us as researchers at the first meetings.

508

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen IT‐pedagogical roles: The IT‐pedagogical project department at VUC has a tripartite role since they are 1) visionary designers for future learning, 2) helping with the actual implementation process in cooperation with the department managers and teachers, for example by participating in the organizing of training courses for teachers and 3) contributing to the evaluation and anchoring of the many IT‐in‐education‐initiatives, e.g. by involving researchers in the development and documentation of the project, as well as in the dissemination of these results. Organizational challenges: The teachers get frustrated when they are faced with new challenges from the organization, and when they are asked to think in innovative ways in relation to the implementation of the new systems. The teachers feel that they are being asked to redefine their teaching role and thereby themselves. Furthermore the teachers miss that the organization decides, establishes, and announces a more general framework on “how we do Global Classroom", rather than each teacher using a personal approach that needs to be negotiated with the students every time.

4. Discussion and findings Our analysis reveals four primary themes:

That the students perceive Global Classroom as motivating because of the freedom / agency to select their own educational environment with the flexibility this provides in their everyday lives.

That the teachers find that their teaching can be carried out in a fairly traditional way, in the Global Classroom setup, but at the same time they find it difficult to change their teaching practices.

That both students and teachers are experiencing communication difficulties and that some of the problems arise because the Global Classroom concept is so close to a traditional classroom that they consequently have high expectations to the communicative "flow" in the learning situation.

That after this start‐up period there is a need for the organization in collaboration with teachers and students to elaborate a more detailed framework that defines and helps establishing a culture of “how we do Global Classroom at VUC”. A culture that works on finding ways to establish clear and sufficient communication and to build upon the good examples of innovative cooperation between the different agents in the educational institution.

Certain characteristics of the VUC students make VUC particularly challenged by dropout issues (VUC 2009; VUC 2011; EVA 2013). These issues make the findings of the students’ positive experiences of the Global Classroom concept essential. For the students and the teachers the start‐up process of the Global Classroom concept has involved so many technical problems that the quality of the teaching was affected. However, evidence from our observations shows that Global Classroom for most teachers today (spring / early summer 2013) operates with few technical problems in daily life, contrary to what the teachers expresses verbally which is perhaps sparked by occasional problems leading to unpleasant loss of control during a lesson. There is an interesting paradox in the different views of the students and the teachers in relation to class activity. Many teachers express that this HF class has students who make a deliberate choice to be at home since this allows them to be somewhat passive in class. While the students suggest that teachers tend not to activate them at home. Both parties may well have the ”right” perception of this experience, as this might be an example of self‐reinforcing pedagogy built on assumptions about a specific group of students without it necessarily being an explicit and chosen pedagogy of the teaching staff.

5. Conclusions and future perspectives VUC Storstrøms transition to the Global Classroom Model has been challenging but has also contributed to the organizations consciousness of needed skills in supporting innovative developments. At the same time, the students have found the Global Classroom concept to have motivational aspects because they have obtained freedom to design their own learning environment. Although students who have chosen the HF‐Global Classroom class to begin with want to continue with this model, there are still technical difficulties. Our study showed that one or more sessions between teachers,

509

Charlotte Lærke Weitze, Rikke Ørngreen and Karin Levinsen students and the technical staff would provide the technical staff with more knowledge about which pedagogical and learning design activities they particularly need to support. Our analysis among other things suggests the need for an enrollment culture showing "this is how" we use video conferencing, while also providing room for a sandbox approach. Moreover, it is essential that the teachers have the opportunity to practice new designs in safe‐zones to get a better sense of what it takes to create activity and motivational training in the Global Classroom concept. The purpose of phase 3 and 4 of the action research process is to implement those activities with workshops and design‐based research approaches. Future perspectives: The use of innovative IT‐pedagogical elements inside the Global Classroom frame could provide further opportunities. Based on the analysis, we argue that play and gamification, and bodily activation with the purpose of motivating the students is worth investigating. This could be explored through the use of learning games, students’ digital productions, role playing, or complex multimodal presentation forms etc. (Koster 2005, Weitze & Ørngreen 2012).

References Andrews, T. and Klease, G. (1998). Challenges of multisite video conferencing: The development of an alternative teaching/learning model. Australian Journal of Educational Technology, 14(2), 88‐97. Darsø, L. (2011): Innovationspædagogik. Kunsten at fremelske innovationskompetence (1.udgave). Samfundslitteratur. Dourish, P. (2001): Where the Action is: The Foundations of Embodied Interaction. MIT Press. Ellingson, D. A., & Notbohm, M. (2012). Synchronous distance education: Using web‐conferencing in an MBA accounting course. American Journal of Business Education (AJBE), 5(5), 555‐562. EVA (2013) ”Almen Voksenuddannelse, evaluering af reformen fra 2009”. EVA (2012): ”Skoler skal hæve ambitionerne”, http://bit.ly/17y1hUC accessed 29.5.2013 Freeman, M. (1998), Video Conferencing: a Solution to the Multi‐campus Large Classes Problem?. British Journal of Educational Technology, 29: 197–210. Gärdenfors, P., (2010): “Lusten att förstå, ‐ om lärende på människans villkor”, Natur & Kultur, Stockholm. Harris and Morrison (2011): “Through the looking glass: adult education through the lens of the Australian”, Journal of Adult Learning over fifty years. Accessed 29.5.2013 at http://www.eric.ed.gov/PDFS/EJ973620.pdf Henriksen, T.D., et al. (2011): ”Har projekter et liv efter deadline? Skoleudvikling fra projekt til forankring” in Cursiv, Nr. 8, 2011, Institut for Uddannelse og Pædagogik, (DPU), Århus Universitet, pp.83‐102. Kjær, C.; I. F. Christensen; R. Blok & L. Petersen (2010): Anvendelse af webkonference på Syddansk Universitet – Erfaringer fra tre pilotprojekter. Tidsskriftet Læring og Medier Årg. 2, Nr. 2 (2009). Koster, R., (2005): A Theory of Fun for Game Design, Paraglyph Press. Laurillard, D. (2012). Teaching as a design science: Building pedagogical patterns for learning and technology. Routledge. Milana (2008): “Initial education and training pathways for Danish adult educators”, ASEM conference 25 November 2008, Beijing, https://pure.au.dk/portal/files/286/Milana‐Marcella‐ASEMworkshopD_paper.pdf accessed 29.5.2013 Rice, K (2011): Making the Move to K‐12 Online Teaching: Research‐Based Strategies and Practices, Pearson. Riis, S. (2012): ”Klasseværelset som eksperimentarium for nye teknologier” i Hasse, C. & Dupret, K. (Red.): Teknologiforståelse ‐ på skoler og hospitaler. Aarhus Universitetsforlag, pp. 87‐110. Roberts, R 2009, 'Video Conferencing in Distance Learning: A New Zealand Schools’ Perspective', Journal of distance learning, vol. 13, pp. 91‐107. Somekh, B.(2008): “Factors affecting teachers’ pedagogical adoption of IT” in Voogt, J. & Knezek, G. (eds.) International Handbook of Information Technology in Primary and Secondary Education, Springer Science p.449‐460. Susman & Evered (1978): "An Assessment of the Scientific Merits of Action Research," Administrative Science Quarterly, (23), pp.582‐603. SWOT personal (2013): http://www.teachingexpertise.com/articles/swot‐analysis‐personal‐note‐489, accessed 2june2013. TDGME (2011), The Danish Government, Ministry of Education: En digital folkeskole, ‐ national strategi for it i folkeskolen, ‐ august 2011. Accessed 29.5.2013 at http://bit.ly/Iq0Z21 VUC (2009): ”VUC og Unge – Politikpapir fra VUC lederforeningen” VUC Storstrøm VUC (2011): ”VUC årsrapporten 2010”, June 2011. VUC Storstrøm VUC (2012): “VUC i tal” VUC Storstrøm, http://bit.ly/15XQ2Sb accessed 29.5.2013 VUC (2013): “Årsskrift 2012.” Marts 2013. VUC Storstrøm. Weitze, C. & Ørngreen, R. (2012): ”Concept Model for designing engaging and motivating games for learning ‐ The Smiley‐ model”, Electronic proceedings in Meaningful Play Conference 2012, Cathegory: Innovation in Game Design, Michigan State University, http://bit.ly/12yp5Xm accessed 29.5.2013 Wiborg, A. (2009): “Varme og beslutsomhed” http://bit.ly/19T02yI accessed 29.5.2013.

510

Social Media as an Educational Tool: Students’ Perspectives and Usage Jan Wiid, Michael Cant and Corinne Nell University of South Africa, Pretoria, South Africa jwiid@unisa.ac.za cantmc@unisa.ac.za nellec@unisa.ac.za Abstract: Students in all tertiary institutions have become more and more adapted to the use of technology. Students are increasingly expecting that their lecturers and the institution that they study at make use of technology in the delivery of study material and the tuition mode. The main aim of this study was to determine students’ perceptions on social media networking systems and their use of it. The study also determined whether students make use of social media networking systems and for what purposes they are using the social media networking systems, as well as whether they believe that it will be an effective and easy way to study course content. These were tested by making use of the Technology Acceptance Model (TAM) constructs namely; ‘Perceive ease of use’, ‘Perceived usefulness’, ‘Attitude towards using’, ‘Intention to use’, and ‘System accessibility’. A survey was distributed to a sample of students in the Western Province region of a leading Open Distance Learning (ODL) institution in South Africa, 198 completed and usable questionnaires were received back. It was found that social media is mostly being used by students for social purposes rather than for educational purposes, and that Facebook is the most popular social media networking system to use. Keywords: social media, social media networking systems, technology acceptance model, students, tertiary institutions, social media platforms

1. Introduction and objectives of the study The past years have seen a revolution in the way education is delivered. The days of a one dimensional offering of knowledge to a passive audience, is long gone. Today’s student is more informed and technology savvy than at any time in the past, and with the advances in technology measured in days and not years, this pace of change is accelerating. The use of internet‐based social media networking systems have enabled companies, consumers, institutions and many more to communicate more effectively and in real time with hundreds, even thousands of other people around the world about a specific topic, product or issue at any point in time (Mangold & Faulds 2009). Social media networking systems do not only make it easy for companies to communicate with their consumers, but also makes it easier for tertiary institutions to communicate related course work to their students, to encourage discussion between and among students and to address administrative issues (Moran, Seaman & Tinti‐Kane 2011; Adamson 2012). Shen, Laffey, Lin and Huang (2006) further indicate that online learning through means of various social media networking systems have become a very common educational format to use by both tertiary institutions and their students around the world, due to its flexibility of time and place. Social media networking systems have the ability to enable lecturers and students to collaborate and share information at any time convenient to them and from any place in the world (Adamson 2012). Adamson (2012) is of the opinion that social media networking systems might change the focus of education from a single student to a group of students, but that students’ individual learning experience is enhanced through collaboration and informal learning with their peers. However, according to Picardo (2011), it is a potential threat that the use of social media networking systems in the tertiary institution can lead to a loss of control for many lecturers, as they experience social media networking systems as being highly disruptive. This may be attributed to the fact that students are more familiar with using different social media tools than the lecturers (Picardo 2011). King, Duke‐Williams and Mottershead (in Picardo 2011) are of the opinion that lecturers may resist the adoption of social media networking systems due to their lack of knowledge. This fact may have an impact on the use of social media networking systems in tuition and it is important to establish the wishes of the students in this regard. The purpose of this study is to determine students’ perception on social media networking systems and their use of it. This study aims to determine, within the context of Unisa:

The perceptions of students on social media as a lecturing tool;

The utilisation of social media by students; and

The relationship between social media as a lecturing tool and the private use of social media by students.

511

Jan Wiid, Michael Cant and Corinne Nell The following section gives an overview of social media networking systems, the uses of social media networking systems, as well as a review on the utilisation thereof by students. The empirical findings and the discussion of the findings appear in the latter part of the paper.

2. Overview of social media networking systems 2.1 Defining social media Social media is defined by Boyd and Ellison (2008) as, “... web‐based services that allow individuals to construct a public or a semi‐public profile within a bounded system, articulate a list of other users with whom they share a connection and view and traverse their list of connections and those made by others within the system”. Mangold and Faulds (2009) on the other hand define social media or ‘consumer‐generated media’ as, “... a variety of new sources of online information that are created, initiated, circulated and used by consumers’ intent on educating each other about products, brands, services, personalities and issues”. It is clear from these definitions that social media networking systems include various online, and word‐of‐ mouth forums which also include blogs, company‐sponsored discussion boards and chat rooms, consumer‐to‐ consumer email, consumer product or service rating websites and forums, internet discussion boards and forums, moblogs (sites containing digital audio, images, movies, or photographs), and social networking websites, to name only a few (Mangold & Faulds 2009). According to Larson (2012) the five most popular social media networking systems used and accepted in the market are; Facebook with 901 million users, Twitter with 555 million users, Google+ with 170 million users, LinkedIn with 150 million users and lastly Pinterest with 11.7 million users. According to Adamson (2012) social media networking systems are an important tool for learning and should be used for this purpose more extensively. Because both students and some lecturers are familiar with social media networking systems, they should take advantage of this as a platform for communication, learning and collaboration, as well as sharing ideas and topics of interest (Adamson, 2012). In order to use social media networking systems effectively, both lecturers and students should be aware of the benefits of social media networking systems. By doing this, it will enable them to communicate with each other on a more effective, flexible and faster way (Laffey et al., 2006).

2.2 The use and benefits of social media networking systems According to Jackson (2011), the use of social media networking systems in classrooms can have a positive psychological effect on students. As soon as students were allowed to answer questions by means of using, for example Twitter, they felt less pressured even though the answer was wrong. Table 1 summarises the ways in which social media networking systems can be implemented in teaching, as well as the benefits thereof. According to Picardo (2011) students’ perceptions and use of technology will play a part in the use or absence of social media networking systems in tertiary institutions. The question still to be answered is; ‘Do students wish to interact with their lecturers online?’. The answer to this question may be more complex than it initially appears to be, as the participation of students in a social media networking system should be voluntary in order to ensure that the necessary quality of interaction and cooperation is obtained for it to improve teaching and learning (Picardo 2011). Picardo (2011) further argues that social media networking systems challenge the ability of both lecturers and students to interact and collaborate successfully via this medium, meaning that when it comes to academia, students do not feel comfortable with the degree of transparency needed in order for the social media networking systems to be effective. In order to determine the students’ perceptions on the use of social media networking systems in tertiary institutions (higher education), the five constructs of the Technology Acceptance Module was studied. These are discussed in the next section.

512

Jan Wiid, Michael Cant and Corinne Nell Table 1: Uses and benefits of social media networking systems Type of social media system

Facebook

Twitter

YouTube

Description of use and benefits Improve communication by enabling students to easily contact lecturers and other students with questions Easily integrate class projects with Facebook through the sharing of books, reviews and promoting student work Use Facebook applications and groups in order to make learning and studying easier and more enjoyable for students Create a Facebook page where you can schedule events, post notes and remind students of important dates and due dates Be a news source by posting status updates and follow other media and well‐known leaders Post additional materials such as links to articles and videos in order for students to continue with their learning even if classes are over Setting‐up specific feeds to enable all students to see and monitor certain events Develop a feed for your students in order to tweet about important dates, upcoming events and assignments, as well as class news Connect with other students, lecturers, as well as parents in order to increase communication and build community Follow tweets of other lecturers in order to keep up with the latest teaching trends, to get ideas and to support one another Share ideas and collaborate with lecturers and students from other classes, schools and departments Use community boards for group projects, as well as brainstorming to enable a number of users to save their resources in one place Allow and encourage students to use Pinterest for presentations and projects Search for inspiring tips on how to organise and decorate your classroom Search, find, pin and organise images, projects, videos, stories etc. for future classes and projects Search for video‐clips under specific topics that can be used in the classroom to give a lesson in a more memorable way Organise playlists to enable students to easily find and watch all relevant and approved videos on a topic Record lessons and post them on YouTube in order for students to review them whenever they want to Create interactive videos by adding quizzes, comments etc. to it

Source: Lepi, K. (2012). 25 Ways teachers can integrate social media into education. [Online] Available from: http://edudemic.com/2012/07/a‐teachers‐guide‐to ‐social‐media/ [Accessed: 13‐02‐2013].

3. Technology acceptance model The Technology Acceptance Model (TAM) is an information system (a system that consists of all the network communication channels used within an organisation) theory that demonstrates how users accept and use specific technology (Davis 1993). The model indicates that when users are confronted with a new software package, various factors influence their decision about how and when they will use this specific technology (Mazhar 2006). Davis, Bagozzi and Warshaw (1989) indicated that user motivation can be explained by three constructs; ‘Perceived usefulness’, ‘Perceived ease of use’, and ‘Attitude toward using the system’. The first construct is ‘Perceived usefulness’ which is described according to Davis (1993) as, “... the degree to which an individual believes that using a particular system would enhance his or her job performance”. The second construct which is ‘Perceived ease of use’ is defined as, “... the degree to which an individual believes that using a particular system would be free from effort” (Davis 1993). The third construct is ‘Attitude towards using’ and is defined as, “... the degree of evaluative affect that an individual associates with using the target system in his or her job”. Two additional constructs include; the fourth construct that was being tested, was that of ‘System accessibility’ which refers to organisational context variables, and the last construct was that of ‘Intention to use’, which refers to the degree to which a person has created a conscious plan to perform or not perform a future behaviour (Venkatesh 2013). These constructs were imbedded in the research study. The next section deals with the research methodology and the findings of the research.

513

Jan Wiid, Michael Cant and Corinne Nell

4. Research methodology In determining the students’ perceptions and usage of social media in context of a leading ODL institution in South Africa, a questionnaire was developed for this study. The questionnaire mostly incorporated questions that are of quantitative nature. The questionnaire was issued to ODL students living in the Western Cape Province and who were registered for 2012 in the College of Economic and Management Sciences. The total population of these students were 12124. The sample size was determined at a confidence level of 95% with a confidence interval level of five. A sufficient amount of questionnaires were distributed, however, only 221 questionnaires were received back, whereof 198 of these were usable responses. Due to this, the confidence interval level has changed to seven. The demographic profile of the respondent group is presented in Table 2 below. The majority of students (29.80 %) were between 18 and 24 years of age. The gender split for the respondent group is female dominated (63 %). Most of the respondents are African (62.63 %). Table 2: Demographic profile Age group 18‐24 25‐29 30‐34 35‐39 40+ Gender Male Female Race African Coloured Indian White

% of Total 29.80% 25.25% 17.68% 15.66% 11.62% 37.00% 63.00% 62.63% 18.95% 4.74% 13.68%

N 59 50 35 31 23 74 126 119 36 9 26

5. Results 5.1 Students’ perception and acceptance of social media as a lecturing tool Students’ perception and acceptance of social media as lecturing tool is based on 21 statements borrowed from the Technology Acceptance Model (TAM). Respondents were asked to rate the 21 statements on a seven point Likert scale (1 being “Strongly disagree” and 7 being “Strongly agree”). The 21 statements are structured as five sub‐constructs; ‘Perceived ease of use’, ‘Perceived usefulness’, ‘Attitude towards using’, ‘Intention to use’ and ‘System accessibility’. For each sub‐construct a mean was calculated to assess the level of agreement among sub‐constructs. The following table shows the means and standard deviations. Table 3: Sub‐constructs ‐ means and standard deviations Sub‐Construct

Mean

StdDev

Ease of use

4.98

1.80

Perceived usefulness

4.50

1.84

Attitude towards using

4.64

1.65

Intention to use

4.44

1.84

System accessibility

4.89

2.10

The sub‐construct ‘Ease of use’ was considered most important with a mean of 4.98, while ‘Intention to use’ was considered least important with a mean of 4.44. However, the means were closely distributed indicating a general agreement on the importance of all the sub‐constructs. The standard deviations are fairly high, indicating variation in agreement among sub‐constructs.

5.2 Reliability of the sub‐constructs Reliability is the consistency of the measurement, or the degree to which an instrument measures the same way each time it is used under the same condition, with the same subjects. The Cronbach’s alpha for the five sub‐constructs all yielded high Cronbach’s alpha values (>=0.80) indicating good reliability. The table below

514

Jan Wiid, Michael Cant and Corinne Nell represents the Cronbach’s alpha values of each of the five sub‐constructs, please note that ‘System accessibility’ is the only one item and cannot be tested for reliability. Table 4: Cronbach’s alpha value of sub‐constructs Sub‐construct

Cronbach’s alpha

Ease of use

0.92

Perceived usefulness

0.92

Attitude towards using

0.80

Intention to use

0.91

System accessibility

none

The individual Cronbach’s Coefficient Alpha value of each dimension is used as a measure of the reliability of the tested dimension. A reliable Cronbach’s Coefficient Alpha value validates that the individual items of a dimension measured the same dimension (concept) in the same manner (or consistently).

5.3 The utilisation of social media by students To determine the utilisation of social media, respondents were asked to indicate the hours of usage per week in categories (no usage, 0‐5 hours, 6‐10 hours, 11‐15 hours, 16‐20 hours and more than 21 hours). The social media platforms were included in the study are Facebook; Twitter; MySpace; LinkedIn and Pinterest. The following table demonstrates the usage of social media. Table 5: Usage of social media by students Social media platform Facebook Twitter MySpace LinkedIn Pinterest

N 60 164 183 170 190

Do not use % of Total 27.15% 74.21% 82.81% 76.92% 85.97%

N 161 57 38 51 31

Use % of Total 72.85% 25.79% 17.19% 23.08% 14.03%

Most respondents used Facebook (72.85 %), with Pinterest (14.03%) as the least used. The following table demonstrates the students’ usage of social media, categorised by hours per week. Table 6: Students’ usage of social media, categorised by hours per week Social media platform Facebook Twitter MySpace LinkedIn Pinterest

Hours 0‐5 % of Total 50.93% 75.44% 81.58% 76.47% 80.65%

6‐10 % of Total 23.60% 10.53% 13.16% 11.76% 12.90%

11‐15 % of Total 11.80% 10.53% 2.63% 7.84% 3.23%

16‐20 % of Total 4.97% 1.75% 2.63% 3.92% 3.23%

21+ % of Total 8.70% 1.75% 0.00% 0.00% 0.00%

Most respondents used social media between 0‐5 hours per week. Twitter and Facebook seems to be used more than the other social media networking systems. The following share chart (Figure 1) produces a visual representation of the hours per week. 5.3.1 The utilisation of social media in education In order to determine the utilisation of social media in education, respondents were asked to indicate how important it is for them to communicate with lecturers by means of social media platforms such as Facebook, Twitter, MySpace, LinkedIn and Pinterest. Table 7 illustrates the usage of social media in education. The majority (77,37% ‐ Unimportant plus Least Important) of the respondents indicated that they are not willing to communicate with lecturing staff by means of social media. Only 9.49% indicated that they are willing to do so while 13,14% had no opinion on this matter.

515

Jan Wiid, Michael Cant and Corinne Nell

Figure 1: Social media utilisation Table 7: Willingness to communicate with lecturers by means of social media platforms Method of Instruction Social Media

Most Important

Important

Neutral

Unimportant

Least Important

% of Total

3.65%

5.84%

13.14%

21.17%

56.20%

5.4 Profiling the usage of social media by respondents The biographical variables age, gender and population group were tested against the usage of social media. Only age group showed significant differences in usage. 5.4.1 Comparison of the respondent’s usage of the social media types Chi‐Square tests were used to test for association between usage and the biographical variables. The purpose of a Chi‐Square test is to test if any significant association exists between usage and a biographical variable. The following share chart (Figure 2) produces a visual representation of the association between usage and age group.

Figure 2: Association between usage and age group

516

Jan Wiid, Michael Cant and Corinne Nell From the Chi‐Square test probability values (p‐values) were produced. A ‘p‐value’ smaller than 0.05 indicates a significant association between the biographical variable and usage at a 95% level of confidence. The significant differences are shown in Table 8. Table 8: Significant differences Social media

Chi‐Square value

P‐value

Significance

Facebook

10.89

0.0279

Significant

The table below shows the usage of Facebook by the different age groups. Table 9: Age group usage of Facebook Count 18‐24 25‐29 30‐34 35‐39 40+

Do not use 8 10 8 4 10 40

Use 53 40 27 27 13 160

Total 61 50 35 31 23 200

A significant association exists between age group and Facebook usage. Clearly the proportion of students that use Facebook is much lower for respondents 40 years and older (13/23=56.52%) than the other age groups (18‐24 years: 53/61=86.89%, 25‐29 years: 40/50=80%, 30‐34 years: 27/35=77.14%, and 35‐39 years: 27/31=87.10%).

5.5 Relationship between views on social media as a lecturing tool and the private use of social media by students In order to explore the relationship between views on social media as a lecturing tool and the private use of social media by students, the overall usage was calculated by coding all the students that used any of the social media type as ‘Use’ and students that didn’t use any of the social media types as ‘Do not use’. The findings revealed that 23.01 % of students didn’t use any social media. The figure below illustrates the ‘Use’ and ‘Do not use’ levels of social media. Social Media Usage Frequencies Level Do not use Use Total N Missing 2 Levels

Count 51 170 221 0

Prob 0.23077 0.76923 1.00000

Figure 3: Social media usage A multivariate analysis of variance (MANOVA) will be used in order to explore the differences between the different sub‐constructs’ mean scores of students ‘using’ and ‘not using’ social media. The profile plot from the MANOVA shows the least square means, as shown in the figure below.

517

Jan Wiid, Michael Cant and Corinne Nell 7

Do not use Use

Social media Usage LS Means

Ease of use score

Usefulness score

Attitude score

Intention score

Accessibility score

Responses

Figure 4: Profile plot of social media usage Table 10 below represents the differences between the different sub‐constructs’ mean scores of students ‘using’ and ‘not using’ social media. Table 10: Social media usage Social media Usage Ease of use score Usefulness score Do not use 4.17 4.14 Use 5.14 4.57

Attitude score Intention score Accessibility score 4.05 3.79 4.11 4.75 4.56 5.03

From the table and profile plot, it is clear that the students who ‘do not use’ social media has lower scores on all the sub‐constructs than the students ‘using’ social media. Students who are active on social media networking systems are in general more positive towards the use of social media. In order to determine whether differences between the means of the students ‘using’ social media and students ‘not using’ social media are statistically significant, separate independent T‐tests were conducted for each sub‐construct. The distributions of the constructs were tested for normality. Because the sub‐constructs were not normally distributed, nonparametric Wilcoxon tests were used instead of T‐tests. These are shown in the table below. Table 11: Differences between the means of social media usage Sub‐Constructs Ease of use Perceived usefulness Attitude towards using Intention to use System accessibility

Social media Usage Do not use Use Mean StdDev Mean StdDev 4.17 2.06 5.14 1.71 4.14 2.08 4.57 1.78 4.05 1.35 4.75 1.67 3.79 1.93 4.56 1.81 4.11 2.15 5.03 2.07

From the Wilcoxon analysis the probability values (p‐values) were produced. A ‘p‐value’ smaller than 0.05 indicates a significant difference of the sub‐construct tested for the respondents’ privately ‘using’ or ‘not using’ social media at a 95% level of confidence. Only significant differences are shown in the table below. Table 12: Significant differences between social media usage Sub‐construct

Chi‐Square value

P‐value

Significance

Ease of use

5.92

0.0150

Significant

Attitude towards using

6.46

0.0111

Significant

518

Jan Wiid, Michael Cant and Corinne Nell Sub‐construct

Chi‐Square value

P‐value

Significance

Intention to use

4.26

0.0389

Significant

System accessibility

4.41

0.0356

Significant

Significant differences between the students ‘not using’ social media and students ‘using’ social media exists for the sub‐constructs ‘Ease of use’, ‘Attitude’, ‘Intention to use’ and ‘System accessibility’ at a 95% level of confidence.

6. Conclusion Due to the fact that technology changes daily, people, specifically students, are more enabled to become informed and aware of the different types of technological systems as opposed to a few years ago. The use of the internet has further enabled many people, students and institutions around the world to communicate more effectively with each other on specific topics and issues at any point in time (Mangold & Faulds 2009). Therefore, it becomes evident that the use of social media networking systems among tertiary institutions does not only make communication easier between them and the students, but it can also add a lot of value in encouraging discussions between and among students, as well as addressing administrative issues (Moran et al., 2011; Adamson 2012). The findings of the study revealed that social media was used more for social purposes than work purposes. Mostly, social media is used between 0‐5 hours per week. Facebook is the most used social media application. Furthermore there were no biographical differences in the use of social media, except for age groups. The age group 40+ used Facebook significantly less than the other age groups. It was also evident that the majority (77.37%) of students do not want to interact with their lectures via the social media networks. Students prefer to use social media networking systems more for social purposes‐ rather than for work purposes and thus they do not feel comfortable with the degree of transparency needed in order for the social media networking systems to be effective in education. The study revealed that the respondents that use social media, considered all the constructs (‘Ease of use’, ‘Attitude towards using’, ‘Intention to use’ and ‘System accessibility’) as significantly more important than the respondents that do not use social media. Students that are well aware of technology might find and perceive social media networking systems easy to work with, whereas students that are not familiar with technology, feel “scared” and “out of control” when using social media platforms. However, the use of social media platforms can aid and enhance the learning experience, especially in higher learning institutions, as face‐to‐ face communication and teaching is limited. The extended use of social media platforms for educational purposes leaves an opportunity for further research. Generally younger age groups put more importance on social media and students that do not use social media put less emphasis on social media. It is recommended that training programmes should be developed in order to aid students in the use of social media for educational purposes. Educational institutions need to develop strategies and tactics to make social media platforms more attractive for students, in order for them to use it for educational purposes. However, it is important that the use of social media platforms in an educational environment ‐ student to student, as well as student to lecturer and vice versa ‐ should be monitored and guided by institutional policies and guidelines, in order to ensure that it is being used effectively and for the right reasons.

References Adamson, C. (2012) “The Role of Social Media in Education”, [online], http://www.icwe.net/oeb_special/OEB_Newsportal/the‐role‐of‐social‐and‐mobile‐media‐in‐education/. Borges, B. (2012) “40 Year History of Social media Infographic Poster”, [online], http://www.findandconvert.com/2012/02/40‐year‐history‐of‐social‐media/. Boyd, D.M. & Ellison, N.B. (2008) “Social Network Sites: Definition, History, and Scholarship”, Journal of computer‐mediated communication, Vol 13, pp 210‐230. Davis, F.D. (1993) “User acceptance of information technology: System characteristics, user perceptions and behavioural impacts”, Academic Press Limited, Vol 38, pp 475‐487. Hobbs, R. (2004) “A Review of School‐Based Initiatives in Media Literacy education”, American Behavioural Scientist, Vol 48. No. 1, pp 42‐59.

519

Jan Wiid, Michael Cant and Corinne Nell Jackson, C. (2011) “Teaching Tolerance: Your students love social media and so can you”, [online], http://www.tolerance.org/magazine/number‐39‐spring‐2011/feature/your‐students‐love‐social‐media‐and‐so‐can‐ you. Larson, D. (2012) “Infographic: Spring 2012 Social Media user Statistics”, [online], http://blog.tweetsmarter.com/social‐ media/spring‐2012‐social‐media‐user‐statistics/. Lepi, K. (2012) “25 Ways teachers can integrate social media into education”, [online], http://edudemic.com/2012/07/a‐ teachers‐guide‐to ‐social‐media/. Mangold, W.G. & Faulds, D.J. (2009) “Social Media; The new hybrid element of the promotion mix”, Business horizons, Vol 52, pp 357‐365. Mazhar, N. (2006) “Technology Acceptance Model”, [online], http://ezinearticles.com/?Technology‐Acceptance‐ Model&id=202354. Moran, M., Seaman, J. & Tinti‐Kane, H. (2011) “Teaching, Learning and Sharing: How today’s higher education faculty use social media”, [online], http://www.pearsonlearningsolutions.com/educators/pearson‐social‐media‐survey‐2011‐ bw.pdf. Park, S.Y. (2009) “An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioural Intention to Use e‐Learning”, Educational Technology & Society, Vol 12, No. 3, pp 150‐162. Picardo, J. (2011) “Teaching and Learning with Social Networks: barriers to Adoption”, [online], http://www.josepicardo.com/2011/08/teaching‐and‐learning‐with‐social‐networks‐barriers‐to‐adoption/. Shen, D., Laffey, J., Lin, Y. & Huang, X. (2006) “Social Influence for Perceived Usefulness and Ease‐of‐Use of Course Delivery Systems”, Journal of Interactive Online Learning, Vol 5, No. 3, pp270‐282. Venkatesh, V. & Davis, F.D. (2000) “A theoretical Extension of the Technology Acceptance Model: Four longitudinal field studies”, Management Science, Vol 46, No. 2, pp 186‐204. Venkatesh, V. (2013) “Unified Theory of Acceptance and Use of Technology (UTAUT)”, [online], http://www.vvenkatesh.com/it/organizations/theoretical_models.asp. Web Tips. (2012) “5 Types of social media users to interact well”, [online], http://web‐tips‐online.blogspot.com/2012/12/5‐ Types‐of‐Social‐Media‐Users‐to‐Interact‐Well.html.

520

Teaching GHG Reduction for the Food Industry to Adult Learners Using Blended Learning Stephen Wilkinson, Duncan Folley, Cathy Barnes, Philip Richard Scott and Quintan Thornton Leeds Metropolitan University, Leeds, UK s.wilkinson@leedsmet.ac.uk d.folley@leedsmet.ac.uk c.barnes@leedsmet.ac.uk Abstract: This research paper investigates the challenges facing the food industry in the UK in namely, recruiting suitable Food Engineers and reducing their Carbon Footprint. It will explore two case studies carried out at Leeds Metropolitan University by two part‐time postgraduate students whilst working full time at their companies. This research is part of the Eco Engineering module taught on the MSc Advanced Engineering Management course. (In both case studies all references to their companies have been deliberately removed).This paper explores how blended learning techniques helped these students and how they achieved a useful analysis and redesign of their companies activities using the PAS2050 GHG reduction techniques and student software. In the case of part time students, the ability to have online resources and tools available is essential in their need for flexible learning, as work commitments may mean they cannot always attend. The drive to increase the number of Food Engineers in the UK is essential to feeding the nation. For example, the National Skills Academy, research has shown that one of the main inhibitors to improvement in productivity is the lack of qualified engineering’s that are industry specifically train. They go on to highlight the growth of automation within the food and drink industry, noting that 67% of companies who supply the large food and drink industry, plan to expand their use of technology over the next two years. Moseley J, 2012, FDF President notes that 20% of those employed in food manufacture are to degree level. Currently UCAS, 2012 shows 132 UK courses which are food related from nutrition, health, food science/technology, with just two courses for food manufacture. This highlights the need for more high quality HE provision to supply the food and drink industry. Keywords: adult learners, Web 2.0 tools, online learning, carbon footprint, food and drink, simulation, PAS 2050

1. Introduction This section investigates the reasoning behind the course design and the approach to delivering such a course. This paper identifies how these techniques can be applied to a course of this type that has part time adult learners working in the food industry. Much research has taken place in defining how successful an online learning environment could be, especially for adult learners. One such study by Artino and Stephens (2009) found that to be successful, students should be autonomous learners, who are self‐motivated and able to self‐regulate their learning experiences. Cercone (2008) has stated features of course design which may suit adult learners, for example, being able to connect current learning to previous experiences, being able to maintain collaboration and interaction between peers, promoting a self‐reflective environment and using advanced self‐regulating learning. Many adults wishing for a more traditional approach to teaching would like more F2F contact. However, the use of asynchronous or synchronous online communications reduce the need for blended learning in which F2F contact is an element. The U.S. Department of Education study (2009) adopted these purely online communication methods and found that the success rate was just as good as a blended approach. Pallof and Pratt (1999) stated that many Universities wish to enable their students to become independent learners in that they wish to move away from the didactic approach. This is where a lecturer is the "sage on the stage " and students are dependent on the lecturer as the font of all knowledge. The reason stated by these authors, is that with a student centred approach, the student becomes more independent i.e. the educator then becomes the "guide on the side". This is supported by Ramsden (2003) who recognises that good teaching encourages students to take control of their own learning and interest in their subject matter. English’s (2008) study of formal University VLEs shows that even though students can be members of many discussion groups, unless there is formal assessment then there is very little activity. If successful this activity

521

Stephen Wilkinson et al. soon tails off after initial interest. This begs the questions why formal University VLEs are so poor at social collaboration and why are informal online communities such as Facebook so successful? The advent of Web 2.0 technology in recent years has seen a dramatic change in the way education now operates, Web2.0 tools, such as blogs, wikis, social networking software, media sharing and others have been responsible for shifting the web to a collaborative work space, where we can all meet to read and write, West (2009). This view is supported by Secker (2007), who described Web 2.0 as an attitude rather than a technology and that as well as developing social networks with content created by the user rather than an organization, it also includes user profiles and the use of tagging to aid the retrieval of items. Anderson (2007) concluded that Web 2.0 was about user generated content and that it harnessed the power of the crowd. The global use of Web 2.0 meant that content was on an epic scale through an architecture of participation through the effect of a network.

1.1 Adult learner Issues This section analyses how most of the above tools are common knowledge to the younger generation but what about the adult learner studying part time? Oblinger (2005) states that young “millennials” are digitally literate and Prensky (2001) has named them “Digital Natives” , ie they have grown up with the web and like the speed of access to information and often multitask whilst one application is working slowly. They like instant feedback, delivery, learn by experience and are impatient with passive activity; they enjoy exploring new tools, especially with peers. They are social and are constant communicators and are more at ease when meeting new people, both in person and online, they are also more open about themselves. Prensky (2001) went on further to label adult learners as “Digital Immigrants” who do not have the millennial advantages However, Kennedy (2008,p.10) concludes that Digital Natives whilst having access to a multitude of technologies don’t always use them in an educational context, for example “few students have high levels of competence across a wide range of applications” and that “familiarity with the use of email does not imply expertise in rigorous online debate and discussion”. This assumption is supported by JISC (2008, p.24) who suggested that digital literacies and information literacies do not go hand in hand when considering the “Google Generation”. Knowles (1984) contradicts Prensky (2001) in that he suggests that adult learners are often attracted to online courses because of convenience and an opportunity to integrate study into their busy lives. They are more goal oriented, self‐directed, more independent and prefer more control over their own learning and don't get involved in activities that are irrelevant, ie “off task”. Other benefits include having more prior work and education experience. These generalisations are criticised by Dede (2004) during a study of a mixed age group of HE students. Dede concludes that many adult learners exhibit “Millennial” learning styles and maintains that categorising these generalisations into groups are a poor foundation for decision‐making about the learning needs of individuals, especially when applied to the cross‐age mixtures of people found in many college and university courses. He found that the difference was in users of technology, ie people of all ages now buy music or groceries online. His surveys found that students preferred F2F teaching to either video conferencing or online learning but voted “distributed learning” (Blended Learning) which uses a mixture of F2F and mediated synchronous and asynchronous communication as being very popular. Asynchronous communication was popular because it gave time for reflection before posting at any time of the day. He found that the use of VLEs was not very popular with students stating shyness and reluctance to participate in these multiuser environments. This view is supported by ECAR (2004) who showed that today’s students can become “wired and tired” through the overuse of technology. (Dede, 2004,p.8) concludes that the “differences among individuals of any age are greater than dissimilarities between age groups “, ie there will always be a mixture of learning styles and while online technologies have proven to be useful, they should be regarded as emergent rather than mature.

1.2 Resulting course design From the previous literature, it is seen that a combination of both Formal University VLE and the more social Web 2.0 tools is required. Other features include both Synchronous communication, such as Skype and Asynchronous discussion areas as featured on both the Wordpress blog and the corridor free to discuss any

522

Stephen Wilkinson et al. course related issue. This enabled mature part time learners to use support from both their peers and lecturers with the flexibility required by students working in this type of industry due to intermittent attendance due to work commitments. The ability to work at home or in their place of work, using Open Source or temporary licensed software is essential to achieve flexibility.

Students registered on University VLE

Skype contact

Feedback and results

The “corridor” peer and staff interaction

WordPress Blog Including comments and interaction Figure 1: Course design

2. The eco engineering scenario The students were presented with a scenario that they had to relate to their own company, to see if they could analyze how their company could reduce GHG. For example: An article by Sanchez J, 2010, illustrates in Figure 2, how an Observatory in Hawaii show that CO2 (Green House Gases (GHG)) levels have risen from approximately 335 to approximately 385 parts per million (ppm) or 1.6ppm/year over the past 30 years. This equates to 30 Gigatons of CO2 being released into the atmosphere per year.

Figure 2: Monthly average C02 concentrate (Mauna Loa Observatory Hawaii Sanchez J, 2010, goes on to discuss how GHG’s have had a disruptive effect on the planet, mainly due to global warming. These effects range from the melting of the polar ice caps, hence the rise in sea levels, change in weather patterns and serious consequences to the future of mankind.

523

Stephen Wilkinson et al.

2.1 Food industry environment issues The level of industrial activity is linked to the generation of GHG. The food industry is no exception, according to the Food and Drink Federation (FDF) the UK’s food and non‐alcoholic drink exports broke through the £10 billion mark in 2010, with an increase of 11.4% in 2011 to £12.1 billion and there are no signs of this growth declining. Formby J. notes that the food and drink industry employs 400,000 people, which in turn is approx 17% of the total manufacturing workforce in the UK. This level of activity results in massive emissions of Green House Gases (GHG). As a responsible nation, we must make as much effort as possible in reducing these emissions. This can be achieved by following the PAS2050, 2008, directive and current European Legislation. This research paper will therefore investigate what UK Higher Education (HE) is doing in preparing their graduates for these future legislations and hence help contribute to the safeguarding of our planet. Case studies will illustrate what can be done, by following the PAS2050 guidelines using software analysis. A course called MSc Advanced Engineering Management has a number of part time students from industry who have developed projects directly related to the food and drink industry. Although all references to their sponsoring company have been removed what the reader should note is the scope and opportunities HE can offer through highly qualified undergraduates and post graduates.

2.2 What can be done? Resource efficiency– a business imperative, 2008, shows how resources will become scarcer and more expensive in the future i.e. Global demand for resources is increasing as the world population grows towards 9 billion people. We need to be more efficient in the use of these resources i.e. save, recycle, substitute, reduce and value our scare resources. This document shows that this initiative has already had an impact, the growth in activity in Europe has increased by 40%, however the use of resources has reduced by 10%. PAS2050, 2008 is a publicly available specification for assessing product life cycle GHG emissions, prepared by BSI British Standards and co‐sponsored by the Carbon Trust and the Department for Environment, Food and Rural Affairs (Defra). The main areas of interest are:

The measurement of GHG, across the lifecycle of a product, from the creation of the material, to the manufacture, transport, in life use, to the final disposal is out lined by this standard.

Measuring the carbon footprint of products across their full life cycle is a powerful way for companies to collect the information they need to:

Reduce GHG emissions.

Identify cost savings opportunities.

Incorporate emissions impact into decision making on suppliers, materials, product design, manufacturing processes, etc.

Demonstrate environmental/corporate responsibility leadership.

Meet customer demands for information on product carbon footprints.

Differentiate and meet demands from ‘green’ consumers.

PAS2050, 2008, Figure 2, shows the lifecycle of a croissant, from the transport of the wheat, to the disposal of any waste.

This technique enables any industry to analyze and then change their practices, in order to reduce GHG.

2.3 GHG analysis tools and techniques Carbon Foot Print Analysis software. There are not many techniques for analyzing this method, other than traditional spreadsheets with scripts. An automated way of using node based analysis is Umberto. There is a 30 day trial and a 3 month student license.

524

Stephen Wilkinson et al. Umberto Umberto is software that uses its own database of data for material, power, transport and recycling costs for a variety of examples. The software can use the lifecycle method as outlined in PAS2050. Two case studies showing student work using umberto are shown below.

Figure 2: Umberto (PAS2050, 2008) 2.3.1 Case study 1 Local production of flour vs. production in Canada This example is based upon the fact that a major bread production company in the UK, source flour from Canada as they believe that the properties of the flour allow for a better quality of product. However this leads to increased transportation of the flour so by applying PAS 2050 to the process of transporting the flour a direct comparison can be achieved to establish if this method does however increase GHG emissions. For the following calculations, the GHG has been calculated for a standard bulk shipment of 10,000 tons of flour for both scenarios. Local production When calculating the GHG output for locally produced flour the following information was entered into umberto:

Wheat plus electricity combined to produce the flour.

The flour is transported 100km to the factory by truck, this results in 400 trips.

Total distance traveled is 40,000km.

The screen shot below shows the configuration of this with the calculated GHG value per ton. As can be seen from Figure 3, as a direct part of this production process the GHG emissions total 7835.85Kg. The next stage of the assessment is to carry out a further test to see how another scenario will perform.

525

Stephen Wilkinson et al.

Figure 3: The configuration for local production Canadian Production When calculating the GHG output for Canadian produced flour the following information was entered:

Wheat plus electricity combined to produce the flour.

The flour is transported 100km to the docks by truck, this results in 400 trips.

The flour is transported 6000km to from Canada to UK by bulk shipment.

The flour is transported 100km to the factory by truck, this results in 400 trips.

Total distance travelled is 86,000km.

The screen shot below shows the configuration of this with the calculated GHG value per ton

Figure 4: The configuration for Canadian Production As can be seen from the above, the GHG value for one ton of flour transported to the bakery is 13770. 57Kg.

526

Stephen Wilkinson et al. Analysis of results From the results above, it can be seen that the Canadian produced flour gives off nearly twice the CO2 when compared to the locally produced flour. So it can be seen how valuable the data is when making global decisions regarding the choices that we have to ensure the sustainability of what and how goods are produced. From looking at the results though, it can be seen that it is not the shipping of the flour that adds the extra GHG output as this accounts for 6000kms whereas the trucks add 40,000km each to transport the same amount of flour due to their reduced payload. Options for improvement;‐

Source the flour from a local supplier providing that they can produce flour to the required quality.

Reduce the transportation of the flour by truck, investigate the possibility of producing the flour closer to the docks so reducing the transportation required.

Perform further comparisons using software to look for alternative methods of transporting the flour, eg rail.

2.3.2 Case study 2 For this study a similar PAS2050 analysis is carried out to determine the environmental impact on transporting the finishing process (enrobing) of a confectionary product to factory A versus enabling the finishing process to be conducted at the local factory B where it is currently being manufactured. The first analysis for manufacture at factory A is shown below in Figure 5.

Figure 5: The first analysis for manufacture at factory A As can been seen from the Umberto analysis, shown by the screen shot above, we get a carbon foot print 0.974 grams per 2.5kg outer of the product. Another analysis was done just for the manufacturing process. If we take the carbon foot print of this model from the one with transportation, this will give a true reflection the transportation is having on the carbon foot print.

Carbon Foot Print of Manufacturing Process = 0.841g per 2.5 Kg/ Outer

Carbon Foot Print of manufacturing at factory A = 0.974g per 2.5 Kg/ Outer

Carbon Foot Print of distance traveled to warehouse = 0.974‐0.841 = 0.133g per 2.5KG/ Outer

The analysis for the manufacture of the product at Factory B is shown below in Figure 6.

527

Stephen Wilkinson et al.

Figure 6: The analysis for the manufacture of the product at Factory B From this model the carbon foot print is given as a 14.65Kg per outer. Again if we subtract the carbon foot print given from the manufacturing model, we see virtually no difference as we only lose 0.841 grams from 14650 grams of carbon giving 14649.159 grams. This is 586% the outers weight in carbon required just to transport the raw materials and the finished product between factories. The requirement to make new moulds to make the product at Factory B is shown in Table 1 and the GHG analysis for the manufacture and transportation of moulds is shown in Figure 7. In this model we have taken from Umberto’s material library Polycarbonate, the energy required to injection mould a product and the weight of each of the moulds. The transportation is from a local plastics manufacturer based 22 km away. From this analysis we are given the carbon foot print of a total of 12.57kg per mould. For a typical production volume, this would equate to only an additional 8.8gramms of carbon added per outer by creating the moulds to mould the product. This is far less than the impact of producing the product at factory B and transporting all the raw materials and finished product between the two sites. Table 1: The requirement to make new moulds to make the product at Factory B Mould Weight

2.5Kg

Electricity used by injection moulding process Distance to between factory A and local Supplier Distance Traveled Empty

0.11 KWH

Number of moulds created

1500

22km

100%

2.3.3 Case study Conclusions Case study 1 The difference between importing and using locally sourced wheat is 13770. 57Kg ‐ 7835.85Kg = 5934.72Kg per ton For a typical 10,000 tonne bulk transport = 59347.2 tonnes of extra Co2 emitted, due to importing wheat. Last year the UK imported 2million tones of bread quality wheat due to bad weather, hence poor wheat production in the UK, Guardian December 4th,2012.

528

Stephen Wilkinson et al.

Figure 7: The GHG analysis for the manufacture and transportation of moulds Case study 2 Shows how it would have a significant impact on enabling the local factory to be tooled up to finish (enrobe) a product, rather than transport it over a great distance to have the same process done. The current carbon footprint due to transporting to Factory A is nearly 6 times the manufacturing carbon footprint. The additional CO2 generated by manufacturing the moulds to enable the finishing process is very small and justifies keeping the finishing process local. One of the main conclusions to make is ensure the localization of material supply and manufacture. The current burning of fossil fuel to transport basic food components 1000,s of kilometers is not sustainable. For example 27% of the UK’s total GHG emissions of 607.2 million tonnes of C02 equivalent, is due to domestic transport (20%) and international transport (7%) of goods, Department of Transport, 2012. This research has shown what UK Higher Education (HE) is doing in preparing Food Engineers for future GHG legislations and hence helps contribute to the safeguarding of our planet.

3. Blended learning conclusions 3.1 Web 2.0 tools The ability for the student to share their work via an online blog, enhances the community of practice aspect of this learning. Students were able to share information and present their work in a highly organized way, using the same drop down menus as found in web pages. Comments for each section are also given; this aids the collaboration between peers and formative tutor feedback. Other essential functions such as security password protect can also be found with this tool. This enables students from industry to protect their companies Intellectual Property or other such sensitive data.

3.2 University VLE This was very good for giving formal feedback and marks to the students. The course was designed as an individual CRN with all the modules being given an individual folder. This meant that students were aware of their progress through the course and the overall view of future workload. Staff found this very useful as it was a one stop shop for all things to related to the MSc in Advanced Engineering Management.

529

Stephen Wilkinson et al.

3.3 Part time student software The availability of Open Source or similar student friendly software is essential if flexible delivery and learning is to be achieved. This can be achieved using free or time limited versions. Many companies now will also provide full version that are time limited, enough for the student to complete their assignment.

3.4 Credibility with Industry The ability to deliver “Live projects” that are relevant to the industry students work in is essential, in terms of both ensuring that students are regarded as contributing to the development of their company and for the students own promotion prospects In investigating such a critical problem as global warming, students will be more aware as global citizens and how their own companies contribution to the reduction of GHG is essential, especially with future legislation.

References Anderson P (2007), What is Web 2.0? Ideas, technologies and implications for education , JISC, Technology & Standards Watch Artino, A & Stephens, J, (2009) Academic motivation and self‐regulation: A comparative analysis of undergraduate and graduate students learning online. Internet and Higher Education, 12, 146‐151. British Standards Institute (BSI) (2008), Guide to PAS2050. How to assess the carbon footprint of goods and services, London, BSI Carey, J. (2012) GLOBAL WARMING: Faster Than Expected?. Scientific American. [Online] Vol. 307, Issue 5 Cercone, K. (2008). Characteristics of adult learners with implications for online learning design. AACE Journal, 16(2), 137159. Deed C (2004) Planning for “Neomillennial” Learning Styles: Implications for Investments in Technology and Faculty Harvard Graduate School of Education August, 2004 Department of Transport (2012), http://assets.dft.gov.uk/statistics/series/energy‐and‐ environment/climatechangefactsheets.pdf ECAR (2004), ECAR study of Students and Information Technology, 2004: Convenience, Connection and Control, Volume 5, 2004 English R et al (2008), Facebook Goes to College: Using Social Networking Tools to Support Students Undertaking Teaching Practicum, MERLOT: Journal of Online Learning and Teaching. Vol 4, No 4, Dec 2008 Food and Drink Federation (2011). “Delivering sustainable growth through exports”. On‐line http://www.fdf.org.uk/corporate_pubs/Exports_brochure_2012.pdf. Accessed 8/3/2013 Formby, J (2012). “Food Drink and Tobacco”. On‐line http://www.unitetheunion.org/how‐we‐help/list‐of‐sectors/food‐ drink‐and‐tobacco/. Accessed 8/3/2013 Hall H & Davison B (2007) Social software as support in hybrid learning environments: the value of the blog as a tool for reflective learning and peer support, Library and Information Science Research 29(2) JISC. (2008). information behaviour of the researcher of the future, A Cyber briefing paper, UCL, January 2008. Kennedy G, Judd T, Churchward A, Gray K, Krau (2008), First year students experiences with technology, are they really digital natives ? Australasian Journal of Education al Technology, 2008,24(1),108‐122 Knowles, (1984) The adult learner, the neglected species, Houston, TX, Gulf Moseley, J (2012), “Skills and Talent in the food and drink industry” On‐line Oblinger,D and Oblinger J (2005), Is it age or IT: First steps towards understanding the net generation. In Educaring the net generation (pp.2.1‐2.20) Boulder, CO: Educause. Sna Fransico: Jossey Bass. Palloff, R.M., & Pratt, K. (1999). Building learning communities in cyberspace: Effective strategies in the classroom. San Francisco: Jossey‐Bass Publishers. Prensky, M. (2001) Digital Natives, Digital Immigrants, On the Horizon (MCB University Press, Vol. 9 No. 5, October 2001 Ramsden, P ( 2005) Learning to Teach in Higher Education ( second edition), Routledge Falmer Publishers Resource efficiency– a business imperative (2008) EEC publications. Sanches, J., and Sanchez, M. (2010) Climate Change: A Challenge for Design. Interactions. August 2010. pp18‐21. Secker J (2007)Social Software, Libraries and distance learners: literature review, London School of Economics and Political Science LASSIE: Libraries and Social Software in Education Smyth K (2007) TESEP “The 3E Approach”, Expert Group, TESEP in Practice Ullrich C (2008) Why Web 2.0 is Good for Learning and for Research: Principles and Prototypes ,WWW '08: Proceeding of the 17th international conference on World Wide Web U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. (2009). Evaluation of evidence‐ based practices in online learning: A meta‐analysis and review of online learning studies. Washington, DC: Center for Technology in Learning. Wenger, E. (2002) Communities of practice: Learning, meaning and identity. N.Y.: Cambridge University Press. West J and West M, (2009) Using Wikis for online collaboration, The power of the read write web, Jossey Bass guides to onlineteaching and learning, Jossey Bass.

530

E‐Learning and Life‐Long Learning: A Descriptive Case Study From a Teacher Educator’s Perspective: 1995‐2013 Eleanor Vernon Wilson Curry School of Education, University of Virginia, USA evw2u@virginia.edu Abstract: This paper describes the author’s perceptions of the impact of technology on her role as a teacher educator. Through the use of autobiographical narrative, the paper illustrates ways in which responding to iterations of e‐learning over time have led to intended and unintended consequences for the author’s personal pedagogical development. The author identifies three areas in which e‐learning has resulted in significant change in her professional life: classroom instruction, developing field placements, and relying on electronic communication to develop distance programs for preservice students. The paper resulted from an examination of current requirements in a teacher education program and st reflects on the demand for critical changes needed in teacher education for the 21 century. The concept of TPCK provides a dynamic framework for such change. Recognizing the vast disparity in current teacher education programs today, what are ways in which the educational community can engage current and future teachers in dialogue that will reflect the need to implement dynamic approaches in e‐learning? Equally important, what are some of the most effective ways to include current teachers in this discussion of the varied aspects related to e‐learning? Keywords: teacher educator development

1. Introduction The past twenty years of my life as teacher educator parallel developments in e‐learning in the broadest sense of this term: acquiring facility with technology skills for personal and instructional purposes and introducing preservice students to technology applications for use in their future classrooms. The call for papers for the 2013 ECEL conference track on Life Long learning led me to review the impact of adapting to increasingly complex technologies on my personal and professional life as a teacher educator. Encouraging and envisioning future challenges for students and colleagues in an increasingly paperless future are key to progress and growth in all areas. It is my hope to discuss with other conference participants the ways in which the development of life‐long e‐learning can be supported and expanded across many disciplines. It is hardly possible to pick up a paper or read an e‐journal that comes in to one’s electronic mailbox today without addressing advances in technology and the pros and cons of the efficacy of technology applications in education. Much debate of course continues to focus on strengths and challenges of using technology, all of which I find helpful in my teaching. From the stance of my professional development as teacher educator, I use the term ”e‐learning” as it has become a unifying theme in all classes I teach. The following discussion focuses on the influence of developing technology skills in three aspects of my work: incorporating strategies in classroom instruction, creating new field based assignments for preservice students, and finally, other uses of technology in electronic and distance communication. In so many ways, it is fair to say, challenges posed by incorporating effective use of technology have led me in directions that have significantly changed my pedagogical approach to working with preservice students.

2. Theoretical framework In this paper I describe intended and unintended consequences of implementing integration of technology in a teacher education program as they have affected my personal and professional development. Advances in technology have paralleled my professional development as a teacher educator in many ways, serving as significant catalysts for change in my personal approach to teaching. The construct of Technological Content Knowledge Pedagogical (Koehler & Mishra, 2009) is a timely and pertinent illustration of the ways in which I currently strive to integrate content with technology. The paper is framed in autobiographical approaches for research related to teacher education practices. Bullough & Pinnegar (2000) suggest guidelines for autobiographical forms of self‐study research, emphasizing the importance for autobiographical self‐studies to ring “true and enable connection” for the reader. Noting the closeness, at times, of autobiography to fiction, they remind us of the importance of narratives that challenge the reader to relate to the writer’s main themes.

531

Eleanor Vernon Wilson Such essays may prod a reader out of one’s ‘comfort zone’ and encourage the reader to take steps to change practices perhaps not previously followed (Kaplan, 2003).. The founding of the academic group focusing on self‐study practices as a Special Interest Group (SIG) of the American Educational Research Association, followed by the publication of the journal, Studying Teacher Education: A Journal of Self‐study of Teacher Education Practices has led to a significant body of research describing the efficacy of self‐study practices. These practices encourage systematic collection and analysis of data on which one can base decisions relative to successful instruction (Dinkleman, 2003 among others). In this instance, collecting artifacts related to previous years of teaching including class syllabi, course evaluations, course work products and the like, provided a framework for reflecting on changes in my instructional practice. Loughran (2007) reminds us that teacher education should be “a crucible in which the very practice of teaching and learning about teaching is the source of sustained inquiry and development.” He emphasizes the importance of “exposing one’s vulnerability” in learning as central to developing practice as one develops a pedagogy of teacher education. It is this sense of personal vulnerability and insecurity with technology in particular that has led to many changes in my personal approach to teaching.

3. Background The path of my professional life has been in many ways one of an ‘accidental tourist’: a participant in a series of opportunities leading to my current position in which I hopefully am no longer a tourist or visitor but a member of a community of educators (Schon, 1987; Samaras, 2002; Dinkleman,2003; Tomlinson, 2011). I completed a PhD twenty years ago after fifteen years of elementary classroom teaching and began the second part of my professional career as a teacher educator, filled with the desire to impart any and all relevant information on effective teaching practices to my (somewhat unsuspecting and sometimes overwhelmed) students. Currently I am an instructor in several elementary general methods classes and field experiences. Additionally, for the past five years I have led a study abroad program that provides student teaching placements in the UK. Along the way I ‘transitioned’ from using the once‐comforting lengthy yellow legal pads to the earliest computer available when completing my dissertation, to currently relying on the several forms of e‐communication simultaneously wherever I happen to be for personal and professional use.

4. Discussion The role of pedagogical content knowledge (TPACK) for practicing teachers as well as future teachers is central to all discussions relating to educational reform in the last ten years (Kohl & Mischa, 2009). In 2013, preservice teachers are expected to be facile in their application of technology applications in a variety of ways to enhance learning in their future classrooms. In many cases recent graduates are hired with the anticipation that their skills in this area will provide leadership for faculties they join. In truth, I find many of the aspects highlighted in the discussion of the components of the TPACK framework generalizable to the integration of all varied aspects of curriculum at any level. Substituting ‘teacher education methodology’ for ‘contexts’ in the framework proposed by Koehler and Mishra (p. 63) below illustrates the active intersection of content and technology I emphasize through teaching.

Figure 1: The active intersection of content and technology

532

Eleanor Vernon Wilson

5. Approaches in classroom instruction Personally, and theoretically, I am committed to the ideals of creating opportunities for students to grow in generative knowledge and ways to incorporate such knowledge as they study to become classroom teachers (Perkins, 1999). Critical to viewing the advantages of technology integration is that of changing one’s stance from a didactic approach to an approach that is facilitative and interactive or constructive (Dewey, 1938; Glasserfield, 1989). In hindsight I am able to see how the challenges of effectively implementing technological advances in my teaching served as a catalyst for many changes in my pedagogical approach and teaching over time. Currently I am responsible for all general methods classes and field placements for preservice elementary teachers in our teacher education program. More than 10 years ago I found that several aspects of e‐learning slowly and insidiously crept into lectures and class room discussions in the university classroom as I transitioned from using the long‐revered overheads to using PowerPoint presentations and to interactive white boards. At first an eager champion of PowerPoint ( it certainly helped me organize information and keep discussions on track more smoothly than prior approaches to classroom teaching) I quickly realized the slides weren’t going to make any difference if I wasn’t creating ways to optimize instruction with more involving strategies such as using clickers to promote class discussion. Relying on PowerPoint slides (no matter how brilliant they might be) or other lectern‐comfortable approaches is not enough to engage students of today and future teachers of tomorrow. One needs to model and implement ways to use technology to challenge the learner, not merely to lull the learner into a sublime state of meditation! And while adapting instruction, I also realized that through the use of new technology platforms I was moving from a didactic approach in the classroom to a more interactive and problem‐based approach, incorporating stimulus from web sites and the like to generate discussion in class. More importantly, I was able to demonstrate ways to integrate content from other content courses into the general methods classes through using the technology applications to synthesize lessons. The resulting products helped students integrate information from related subject‐specific classes for teaching their initial practicum. Thus for me, the TPACK diagram above illustrates the ways in which I view technology to be an effective aid for designing, modelling and integrating instruction across all dimensions of content in teacher preparation classes and ultimately in classroom teaching. Becoming more confident of my abilities in this arena, I also urged students to design digital projects of their own. Requiring digital presentations as part of the classes I teach leads to an additional dimension provided challenges for this current generation proved more challenging than I anticipated: it’s one thing to live on one’s i‐phone but another to create engaging and informative academic presentations.Feeling slightly insecure about what I was asking students to do,yet eager to make changes for presenting classroom research papers in a large introduction to teaching class this past year, I assigned students a project in “digital storyboarding.” First of all, I had to become comfortable with this process myself. Then I discovered that students were unfamiliar with this program and spent more time introducing the technology aspects than I anticipated, but in the end was ultimately delighted with the final outcome of these projects. Discussions of topics ranging from equality of access to education for minority groups to single parenting became alive and, more importantly, engendered lively classroom discussions in a way not previously demonstrated. Feedback from the students on this part of the class was positive and reflected their desire to do more if given the possibility. Of the sixty students enrolled in two sections, close to 80% reached the classification of highly effective in analyzing, creating, and illustrating the two sides of controversial educational issues on a rubric designed for the project.

6. Field placements E‐learning served as a stimulus for one of the earliest steps I took when asked to design a new approach to field placements for preservice students. Mindful of the challenges posed by Brush et al (2003) that it is a “daunting task” to provide preservice teachers with meaningful and effective experiences related to technology integration I approached a local elementary school with a proposal for an after‐school technology ‘club.' This club paired preservice students with elementary school pupils as they develop projects related to curriculum studies in science and social studies. At the end of the semester, the pupils, accompanied by the student tutors, gave a presentation for parents and for their home classes. The program lasted successfully for over 8 years and led to my developing several grant proposals for the purchase of laptop computers that are still in use in the school.

533

Eleanor Vernon Wilson Again, incorporating ways to help preservice students work with pupils was reflection of challenges inherent in programs such as this were initially daunting; in hindsight, much of what is now termed TPACK was particularly relevant to this program. The university preservice students were at first frustrated, then overwhelmed, but by mid‐semester were comfortable, with the progress of their pupils’ projects. The strategies related to the process of designing presentations reflected integration of several content areas (e.g. a presentation on weather/geography/stories from a particular area) along with balancing the input of tutors with that of the pupils proved challenging but ultimately, I think, reflected the development an approach mirroring TPACK. The impact of the club’s activities, both for elementary pupils and for preservice students, was evaluated over several semesters (Franklin & Wilson, 2003). Preservice students reported a significant increase in their ability to transfer easily between various computer platforms, a statistically significant increase in their knowledge of how to use to create effective web pages along with PowerPoint presentations. And as noted above, the preservice students also discussed their perception of their role of a facilitator rather than a more traditional instructor as they worked through this process. By the time the semester was over, students had come to recognize the advantages of using technology to integrate information from varied topics to create new, and engaging, presentations. Academic gains reflected in pupil knowledge of the subjects studied (social studies, language arts, science) were noted by classroom teachers, as were the affective gains, most notably pupils’ increased self‐confidence and enthusiasm about sharing and developing the use of technology for future projects. This enthusiasm carried over into classrooms, and teachers soon began using the pupils as ‘experts’ to introduce their peers to varied technology programs. More than ten years ago, Larry Cuban (2001) identified the lack of effective opportunities to introduce classroom teachers to successful adaptations of technology in classrooms. Increasingly, many schools are addressing these issues through local initiatives and. “contextual in‐service” that has the lasting strength leading to changes for practicing teachers (Sheeky et al, 2003). As an addendum to this discussion, over time, I’ve had many occasions to work with teachers in the school where the after school club was placed who still cite the effectiveness of this particular program, an example of informal “contextual” professional development that has lasting impact on teaching and learning.

7. On ‐line, audio, and video technology applications in distance teaching The use of email and other forms of communication has made my life much more streamlined and provides a seamless (in most cases) way to respond to student assignments and activities. However, the challenges of using digital media for classroom instruction, observation of students, and communication with students posed another stimulus for change on my part. Not only did I have to learn to operate a variety of cameras, communications systems, and the like, I also found it initially a challenge to mediate group discussions, give feedback on lessons observed, or engaging in student conferences in a productive way (Brush, 2003). Added to this was my developing an international student teaching placement for students that required by weekly, if not more, conferencing with students individually as well as in a group via electronic communication. Again, becoming more adept with predicting what could go wrong and anticipating the ways I could push myself to address issues through media helped to remind me of different stances one needs to take to achieve one’s goals. The straight‐ forward videoing of preservice students’ teaching, sending clips to me for suggestions is one way to identify progress which can be successful (Pianta et al, 2009). Communicating via Skyping or other forms of distance video conferencing took more energy for me to develop: wait time, connection clarity, and mediating group discussions from afar are another challenge that I’m still working on (Chen and Willits, 2007). The “flipped classroom’ is gradually becoming part of the curriculum in my preservice elementary program, and this is going to be a welcome challenge. Erik Mazur’s discussion (2013) of the flipped classroom on YouTube is not only motivating but reflects an approach I hope to develop in my future classes. And lastly I am about to embark on developing an on‐line course, truly a challenge but one I’m looking forward to! On‐line courses are becoming increasingly important as a way for students to efficiently gain background knowledge in formats that provide flexibility and often at lower costs than the university classroom.

534

Eleanor Vernon Wilson

8. Directions for further research: What is the ‘so what’ of this paper? What did I find out from this process and does this study merit further examination? What are the applications for further work? Why might this particular story interest others and bring something new to the discussion of developing pedagogy of teacher education? And what then, were the intended and unintended consequences of incorporating technology as a central part of my approach to teaching? Using the purposeful steps of autobiographical analysis I have attempted to show the changes I’ve made as I responded to technological advances. (Bullough &Pinnegar, pp. 16‐19). Thinking back over the change in direction much of my teaching has taken, I came to realize the key turning points were brought about through challenges to develop expertise in new ways of learning and teaching through integrating technology applications in my classes, e‐learning at its core. As I was personally challenged in many aspects of technology application, I also challenged future teachers in my classes to use and apply in technology applications their own teaching. While not generalizable beyond this particular instance, I hope others will find similarities in this paper. Incorporating technology tools, even as simple as demonstration slides in PowerPoint, led to more discussion, involvement and interest on the part of preservice students and modelled for them ways in which they might best create their own units of instruction using technology applications. The challenge of establishing the computer club/program for thirty preservice student tutors with thirty elementary school students was, for me, another step. Not only did I need to be familiar with the approach to designing projects, I also was responsible for guiding preservice students in effective ways as they worked with the elementary school students: a reflection of my earliest understanding of ramifications of the concept of technological pedagogical content knowledge. Mastering specific skills was an intended consequence of the steps I’ve taken. The unintended consequences can best be described as leading to more inclusive class sessions, developing strategies to involve and challenge students to think beyond the conventional boundaries of the classroom, the creation of new programs to involve children in schools, writing several grants to purchase portable laptops and carts for the schools where I worked, and establishing an international student teaching placement that required vigilance in communicating electronically. Much as was expressed by a student who participated in the technology club, I feel I’ve moved beyond feeling completely frustrated with certain aspects of technology to more of a comfort zone and application of the ways one can apply technology in learning and teaching. The ‘so what’ of this particular exercise served to remind me of the particular position of teacher educators as they work with future teachers and the responsibility we have of providing preservice students maximum introduction to new, and future, ways of achieving effective learning through the integration of technology. By responding to the three e‐learning challenges I’ve described, I intended to illustrate a framework of personal change and growth for myself illustrative of a path many may recognize and find useful Hopefully, as a result of this work, I’ve moved from a position of an accidental tourist in the profession of teacher education to a more purposeful, effective and reflective member of the community.

References Blanton, W.E., G Moorman, W Trathen ‐ Telecommunications and teacher education: A social constructivist review. Review of research in education, 1998 Bruner, Jerome. (1960, 1999). The process of education. New York: Ventage. Bruner, Jerome. (2004). Life as narrative. Social Research: An International Quarterly of Social Sciences, 71 (3), 691‐710. Brush, Thomas, et al. (2003). Integrating technology in a field‐based teacher training program: The PT3@ASU initiative. Educational Technology Research and Development 51 no 1, 57‐72. Bullough, Robert V. Jr. (1989). First‐year teacher: A case study. New York: Teachers College. Bullough, Robert V. Jr., & Stefinee Pinnegar. (2000) Guidelines for quality in autobiographical forms of self‐study research. Educational Researcher, (30) 3, 13‐21. The Carnegie Forum on Education on Education and the Economy. (1986). A Nation prepared: Teachers for the 21st century. New York: Carnegie Corporation. Chen, Yau‐Jane and Fern K. Willits (2007). A path analysis of the concepts in Moore's theory of transactional distance in a videoconferencing learning environment. Journal of distance education (13)2 51‐65. Darling‐Hammond, Linda. (2000) How teacher education matters. Journal of Teacher Education, 52 (3), 166‐173.

535

Eleanor Vernon Wilson Darling‐Hammond, L., & Bransford, J. (Eds.). (2005). Preparing teachers for a changing world: What teachers should learn and be able to do. San Francisco: Jossey‐Bass. Darling‐Hammond, L. (2006). Powerful teacher education: Lessons from exemplary programs. San Francisco: Jossey‐Bass. Dewey, John. (1938, 1997) New York: Touchstone Dinkleman, Todd (2003). Self‐study in teacher education: A means and ends tool for promoting reflective teaching. Journal of Teacher Education, 54 (6) 6‐18. Erickson, Frederick. (1985). Qualitative methods in research on teaching. Occasional Paper No. 81. Eric document ED263203. Fitzgerald, J. (2001). Can minimally trained college student volunteers help young at‐risk? children to read better? Reading Research Quarterly, (36) 28‐48. Franklin, Cheryl & Wilson, Eleanor Vernon. “Preservice students’ application of technology skills.” Paper presented at the Learning Conference. London, England. July, 2003. Gardner, Howard & Veronica Dyson. (1994). Teaching for understanding in the disciplines‐and beyond. Teachers College Record, (96) 2, 198‐218. Glaserfield, Earnest (1989). In T. Husen &T, N. Postlethwaite. The International Encyclopedia of Education (1) 162‐163. Glenn, Allen D. (2001). Lessons in teacher education reform: A comparative analysis of teacher education in the United Kingdom and the United States. Washington, D.C. American Association of Colleges for Teacher Education. Goodlad, John. (1990). Teachers for our nation’s schools. San Francisco: Jossey Bass. Hamre, Bridget K. & Robert C. Pianta (2001). Early Teacher‐Child Relationships and the Trajectory of Children's School Outcomes through Eighth Grade. Child Development, 72 (2) 625–638. Hamre, Bridget K., Andrew J. Mashburn, Robert C. Pianta & Jason Downer (2006). Teacher and Classroom Characteristics Associated With Teachers’ Ratings of Prekindergartners’ Relationships and Behaviors. Journal of Psychoeducational Assessment, 24 (4) 367‐380. Holmes Group. (1986). Tomorrow’s teachers: A report of the Holmes Group. East Lansing, Michigan. Howey, Kenneth R. & Nancy Zimpher. (1989) Profiles of preservice teacher education: Inquiry into the nature of programs. Albany, New York: SUNY Press. Judge, Harry. (1982). American graduate schools of education: A view from abroad. New York: A Report to the Ford Foundation. Kaplan, Jeffrey S. (2010). Self‐Study of teacher education practices: Towards a personal understanding of critical pedagogy. Unpublished paper. AERA meeting, 2010. Kinsella, Elizabeth Anne. (2006). Constructivist underpinnings in Donald Schon’s theory of reflective practice: Echoes of Nelson Goodman. Reflective Practice, (3) 277‐286. Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 9(1), 60‐70. Loughran, John & Tom Russell. (2007). Looking into teaching about teaching. Studying Teacher Education, 3(1), 103. Loughran, John (2006). Developing a pedagogy of teacher education: Understanding teaching and learning about teaching. London: Routledge Loughran, John & Tom Russell, Eds. (2002). Improving teacher education practices through self –study. London: RoutledgeFalmer McNergney, Robert & Scott R. Imig. (2006). Challenges of Assessing Value Added to PK‐12 Education. Teachers College Record, 12814. October 26, 2006 Kornfield, John & Perry M. Marker, Martha Rapp Ruddell, Thomas Cooke, Phyllis Fernlund (2003). Through the looking glass: Self‐study in an era of Accountability. Teacher Education Quarterly, 30 (3) 2‐22. Kosnik, Clare; Beck, Clive. (2000). Who should perish, you or your students? Dilemmas of research in teacher education. Teacher Education Quarterly, 27 (2), 119‐135. Lortie, Dan C. (1975). Schoolteacher: A sociological study. Chicago: University of Chicago Press. Loughran, John & Tom Russell. Eds. (2002) Improving teacher education practices through self‐study. London: RoutledgeFalmer. Loughran, John. (2006). Developing pedagogy of teacher education: Understanding teaching and learning about teaching. New York: Routledge Loughran, John & Tom Russell (2007) Looking into teaching about teaching. Studying Teacher Education, 3(1) 1‐3. Mazur, Erik. (2010). Confessions of a converted lecturer. Retried from You Tube McCourt, Frank. (2005). Teacher man: A memoir. London: Harper Perennial. Miles, Matthew B. and A. Michael Huberman (1994). Qualitative data analysis: an expanded sourcebook 2nd ed. Thousand Oaks: Sage Publications, 1994 Morine‐Dershimer, Greta. (1989). Preservice teachers’ conceptions of content and pedagogy: Measuring growth and reflective, pedagogical decision‐making. Journal of Teacher Education, 40 (5) 46‐52. Morine‐Dershimer, (1990) Instructional Planning in Classroom Teaching Skills J.M. Cooper (Ed.). New York: D.C. Heath. 17‐ 49. Newmann, Fred M. Helen MM. Marks, &Adam Gamoran (1996). Authentic Pedagogy: and Student Performance. American Journal of Education, (104) 280‐312. Orlofsky, Diane D., (2001). Redefining teacher education: the theories of Jerome Bruner and the practice of training teachers. New York: P. Lang

536

Eleanor Vernon Wilson Perkins, David. (1996). Teaching for understanding. American Educator, 17 (3), 28‐35. Perkins, David. (1999). The many faces of constructivism. Educational Leadership, 57 (3) 6‐11. Piaget, J. (1954). The construction of reality in the child. New York: Basic Books. Pianta, R.C., White, T., & Haynes, N. (2007). The Curry Participant Pool Data Base: Research opportunities for graduate students and faculty [PowerPoint Presentation]. Pianta, R.C. , Bridget Hamre (2009). The Classroom Assessment Scoring System (CLASS)™ Charlottesville, Virginia. Ravitch, Diane. (2007). Challenges to teacher education. Journal of Teacher Education, 58 (4), 269‐273. Rimm‐Kaufman, S. E., Storm, M. D., Sawyer, B. E., Pianta, R. C., & LaParo, K. M. (2006). The Teacher Belief Q‐Sort: A measure of teachers' priorities in relation to disciplinary practices, teaching practices, and beliefs about children. Journal of School Psychology, 44, 141‐165. Samaras, Anastasia P. (2002). Self‐study for teacher educators: Crafting pedagogy for educational change. New York; Peter Lang Schon, Donald A. (1983). The reflective practitioner: How professionals think in action. New York: Basic Books Inc. Shinn, Mark R. (1998) Advanced applications of curriculum‐based measurement. New York: Guilford Press. Sheeky, Arthur, Ed. (2003). How to ensure ed/tech is not oversold and underused. Lanham, Maryland: Scarecrow Press Skinner, B.F. (1968). The technology of teaching. New York: John Wiley Tomlinson, Carol Ann (2010) Notes from an accidental teacher. The effective educator. 68 (|4), 22‐26 Tyler, Anne. (1985). The accidental tourist. New York: Berkley Books. Venn, Martha L.; Moore, R. Larry; Gunter, Philip L. (2001) Using Audio/Video Conferencing To Observe Field‐Based Practices of Rural Teachers. Rural Educator. 22 (2) 24‐27. Vygotsky, Leo S. (1978). Mind in society: The development of higher psychological processes. Cambridge, Ma: Harvard University Press. Wang, J. & Hartley, K. (2003). Video Technology as a Support for Teacher Education Reform. Journal of Technology and Teacher Education, 11(1), 105‐138. Wilson, E.V. and Blasi, L. (2001) “Preservice students’ application of technological skills in school‐based settings.” Journal of the Virginia Society for Technology in Education 15, (3) Yin, Robert K. (2003). Case study research: design and methods. San Francisco: Sage Publications Zeichner, Ken. (1999). The new scholarship in teacher education. Educational Researcher, 28 (9), 4‐1

537

Can e‐Learning Identify Poor Performers in Medical School? Hitomi Yukawa, Raoul Breugelmans, Takashi Izumi and Miki Izumi Tokyo Medical University, Japan yukawa@tokyo‐med.ac.jp Abstract: Tokyo Medical University launched e‐learning in academic year 2011. Our e‐learning system is based on Moodle, an open‐source learning management system. The aim of our e‐learning is to support regular face‐to‐face sessions by providing high resolution materials, self‐assessable quizzes, student class evaluation, and communication tools including message boards and messaging. E‐learning has provided data related to students’ activities, such as frequency of access and time/duration of each activity, which are not obtained through conventional educational processes. In many classes, the students’ level of understanding has been measured only through term‐end examinations. It has, therefore, been difficult to give students with low levels of understanding appropriate educational support from the early phase of courses. However, e‐learning possibly provides data that enable us to identify students who require support early on in a course. In this study, we hypothesized that students who show infrequent access to e‐learning and low achievement on e‐learning tasks tend to obtain low grades on term‐end examinations. We evaluated the relationship between frequency/achievement on compulsory and voluntary e‐learning tasks and term‐end examination grades to test the hypothesis and to give potential poor performers effective support early on. In 2011 and 2012, the Dermatology and Cardiology courses utilized e‐learning for uploading of class materials, quizzes and class evaluations. In Dermatology, quiz assignments were compulsory tasks, while viewing of materials and class evaluations were voluntary tasks. In Cardiology, quizzes and class evaluations were compulsory tasks, while viewing of materials was a voluntary task. We examined the correlation between term‐end examination grades and achievement on compulsory tasks, including quizzes in Dermatology and Cardiology, and class evaluations in Cardiology. Also, we examined the correlation between term‐end examination grades and achievement on voluntary tasks, including view rates of class materials in Dermatology and Cardiology, and class evaluations in Dermatology. Compulsory quizzes in Dermatology/Cardiology and compulsory class evaluations in Cardiology showed no correlation with term‐end examination grades. In contrast, voluntary access to uploaded class materials in Dermatology/Cardiology showed a positive correlation with term‐end examination grades, suggesting that students with lower access to voluntary tasks tend to obtain lower grades on term‐end examinations. With regard to class evaluations in Dermatology, the submission rate showed a correlation with term‐end examination grades. In addition, students’ degree of understanding, one of the questions on the class evaluation questionnaire, demonstrated a positive correlation with the examination grades. These findings suggest that data from voluntary class evaluations may also provide information that identifies potential poor performers. E‐learning provides numerous data that may improve educational efficacy. In this study, data from compulsory tasks showed no difference between task achievement and term‐ end examination grades. Data from voluntary tasks, however, showed a positive correlation with term‐end examination grades. Therefore, data from voluntary e‐learning tasks may be useful to identify potential poor performers in the early phases of courses, thus enabling teachers to more efficiently and effectively provide educational support. Keywords: e‐learning, medical education, Moodle, blended learning, poor performers

1. Introduction Tokyo Medical University launched e‐learning in academic year 2011. Our e‐learning system is based on Moodle, an open‐source learning management system. We customised the system interface to fit into our educational system (Figure 1). We provide a blended type of e‐learning and the aim of our e‐learning is to support regular face‐to‐face sessions by providing high resolution materials, self‐assessable quizzes, student class evaluation and communication tools including message boards and messaging. We firstly introduced this system as a pilot program into Medical English III and Dermatology in the 3rd year of our 6‐year medical school curriculum (Table 1).

1.1 Research motivation Medical students at our university have been evaluated mainly by term‐end examinations. In other words, teachers have not been able to support students who are possibly poor performers until the examination ends. If there is some clue to identify such students from early on, teachers can provide guidance to motivate students to become more involved in study activities and obtain better understanding and higher grades. E‐ learning has the potential to provide a large amount of data that cannot be gained from traditional learning methods. By extracting data to identify potential poor performers from early on, e‐learning may make an additional contribution to our medical education.

538

Hitomi Yukawa et al.

Figure 1: Tokyo Medical University e‐learning portal

1.2 Research question There are various functions in the Moodle system (Table 2) and a huge amount of data can be obtained at once. Still, it is difficult to grasp each student’s level of motivation and level of understanding by simply tracking data. Determining what data should be extracted and compared to identify poor performers is the key to success. It is also essential to identify the conditions of tasks students have been given as well as other factors affecting the students’ activities. For these reasons, we attempted to determine which items are effective in identifying poor performers as early as possible to provide efficient support.

1.3 Limitations of this study We launched e‐learning in October 2011. In academic year 2011, we applied this system to only 2 courses, Medical English III and Dermatology for 3rd year students. In academic year 2012, the system was used in at least one course in each of the 6 years of medical school, in a total of 19 face‐to‐face courses and 24 clinical training courses (Table 1). The number of courses that use this system is increasing. However, it is not yet high enough to sufficiently evaluate the feasibility of the system to identify which functions/data are efficient to be extracted to optimise student outcomes. E‐learning provides freedom of learning anytime and anywhere students wish to learn. However, there are some hurdles that prevent full realization of this freedom. The first hurdle is device possession. According to a st 1 year student survey, only 90.38% of students own smartphones (Figure 2). Tablets and notebook computers may have some limitations to study on‐line from the point of freedom to learn anytime and anywhere due to internet access. The second hurdle is poor infrastructure in terms of internet access. Lecture halls and class rooms have been equipped with wireless internet access at our university, although access capacity is only 50 for each hall and room for about 120 students. Our university hospital, however, does not offer any WiFi service because of concerns of interference with the wireless ordering system. This study was therefore conducted under limited on‐line access conditions, which does, however, reflect a typical Japanese medical school setting. Table 1: List of courses using e‐learning in academic years 2011 and 2012 Basic medicine

2011 Medical English III

2012 Introduction to medicine

Dermatology

Research topics Sports medicine Medical education Physiology I Medical English III Social medicine I Medical English IV Otorhinolaryngology/Oral cavity system

539

Hitomi Yukawa et al. Clinical medicine

2011

Clinical training course

2012 Ophthalmologic/Visual system Dermatology Cardiology Anesthesiology Growth/Development Severe Invasiveness Immunology/Allergies Community‐based learning Hematology/Pulmonology Cardiology Diabetic medicine/Endocrinology Metabolism/Rheumatology/Asthma/Allergy/Neurology Hematology Gastrointestinal medicine Dermatology Psychiatry Pediatrics Laboratory medicine Radiology Geriatric medicine Nephrology Neurology Respiratory surgery/Endocrine surgery Orthopedic surgery Urology Ophthalmology Otorhinolaryngology Obstetrics and gynecology Anesthesiology Plastic and reconstructive surgery Emergency and critical care medicine Pathology

2. Methods 2.1 Target courses and students To determine efficient data for comparison, we selected two courses that employed the same functions. Dermatology for 3rd year students in the latter half of academic year 2012 and Cardiology for 4th year students in the first half of academic year 2012 used the same e‐learning functions: uploading class materials, quizzes and class evaluation. Both subjects are categorized as clinical courses, which suggests that they may have several common features, which may differ from these of basic science courses.

2.2 Target data for comparison The Dermatology and Cardiology courses utilized e‐learning for uploading class materials, quizzes and class evaluations. In Dermatology, quiz assignments were used as compulsory tasks, while viewing of materials and class evaluations were used as voluntary tasks. In Cardiology, quizzes and class evaluations were compulsory tasks, while viewing of materials was a voluntary task. We examined the correlation between term‐end examination grades and achievement on compulsory tasks, including quizzes in Dermatology and Cardiology, and class evaluations in Cardiology. Also, we examined the correlation between term‐end examination grades and achievement on voluntary tasks, including access rates of class materials in Dermatology and Cardiology, and class evaluations in Dermatology. In addition, we reviewed two items on students’ class evaluation questionnaires; level of understanding of the content of the classes and level of interest in the classes to see whether these answers had any relation to students’ grades.

540

Hitomi Yukawa et al. Table 2: Functions embedded in Moodle and used in the e‐learning system Message board Course syllabus Course schedule

Class material upload Class evaluation Quiz

E‐mail

Discussion

2.3 Division of students into groups To compare task completion rates and students’ grades we divided the students into groups. According to the results of term‐end exams, students were divided into five groups; Group A consisted of students with a score of 90‐100%; Group B consisted of students with a score of 80‐89%; Group C consisted of students with a score of 70‐79%; Group D consisted of students with a score of 60‐69%; and Group F consisted of students with a score under 60%. In Cardiology, almost half of the students failed and were included in group F, and there was no Group A (Figure 3).

Figure 2: Ratio of smartphone users

2.4 Design of this study Students’ achievement of both compulsory and voluntary tasks and the results of term‐end examinations were compared to determine which data correlated with the grades. In other words, frequency of access and/or achievement of tasks were evaluated according to term‐end examination grades. In addition, we reviewed each item of class evaluation. We compared the results of “level of understanding” and “level of interest” and evaluated whether these answers had any correlation to the grades of term‐end examinations. Compulsory tasks were quizzes in Dermatology/Cardiology and class evaluations in Cardiology. Students’ achievement of the tasks showed no correlation with term‐end examination grades (Figure 4‐6). They showed high achievement levels.

3. Results 3.1 Results of compulsory tasks

Figure 3: Students’ division into groups according to term‐end examination grades

541

Hitomi Yukawa et al.

3.2 Results of voluntary tasks Voluntary tasks were access to uploaded class materials in Dermatology/Cardiology. They showed a positive correlation with term‐end examination grades, suggesting the students with higher access to voluntary tasks tended to obtain higher grades on the term‐end examination, and suggesting that students with lower access on voluntary tasks tend to obtain lower grades on the term‐end examination (Figure 7‐9). For class evaluation submission in Dermatology, the difference was less marked as for accessing class materials.

3.3 Results of class evaluation With regard to class evaluations in Dermatology, students’ level of understanding, one of the questions on the class evaluation questionnaire, demonstrated slight differences between the group that passed the examination and the group that failed the examination (Table 3). Students’ level of interest showed the same tendency. These findings suggest that data from voluntary class evaluations might also provide information that identifies potential poor performers. 20.00%

40.00%

60.00%

80.00%

100.00% 93.15%

90.61%

91.63%

90.00%

88.44%

Figure 4: Compulsory task 1 – Dermatology quiz submission rate

Figure 5: Compulsory task 2 – Cardiology quiz submission rate

542

Hitomi Yukawa et al.

Figure 6: Compulsory task 3 – Cardiology class evaluation submission rate

Figure 7: Voluntary task 1 – Dermatology class materials access rate

Figure 8: Voluntary task 2 – Cardiology class materials access rate

543

Hitomi Yukawa et al.

Figure 9: Voluntary task 3 – Dermatology class evaluation submission rate Table 3: Difference in class evaluation results between student groups (5‐point evaluation) Student group by exam results A (90‐100%) B (80‐89%) C (70‐79%) D (60‐69%) F (0‐59%)

Q. “The class content was understandable” 4.25 4.07 4.16 4.35 3.80

Q. “The class content was interesting” 4.29 4.11 4.20 4.30 3.65

4. Discussion The advancement of information and communication technology (ICT) has given us more opportunities to select ways of education. Distance learning technology could provide an avenue for doing this without losing travel time (Ozuah 2002). In medical education, however, ICT can be effectively combined with face‐to‐face learning and/or other technologies to provide blended learning. It was demonstrated that blended learning is recommended as optimal for education in the medical environment (Rusnakova 2012). In addition, there is evidence for the effectiveness and acceptance of e‐learning within the medical education community, especially when combined with traditional teacher‐led activities in a blended learning educational experience (Ruiz 2006). Even in a study in which students had a neutral attitude about web‐based instruction, students were mostly positive about the convenience of web‐based instruction and the ability to control their pace of learning (Shih 2001).One study reported that students preferred to use electronic means to seek help and that they found it more effective (Kitsantas 2007). Learning delivery is the most often cited advantage of e‐learning and includes increased accessibility to information, ease in updating content, personalized instruction, ease of distribution, standardization of content, and accountability (Delialioglu 2004). Educators will no longer serve mainly as the distributors of content, but will become more involved as facilitators of learning and assessors of competency (Rowe 2012). On the other hand, there have been reports that integrating technology into healthcare education has the potential to develop non‐technical skills that are relevant for clinical practice, though students currently lack the experience and insight to use technology effectively and find it easy to neglect them (Arianne 2008). Also, there is a complicating factor related to students’ confidence with using computers and web‐based activities (Delialioglu 2004). For teachers, creating contents and/or uploading materials are often perceived to be laborious. Tokyo Medical University launched blended style e‐learning in 2011. In order to enhance usage of this system both for students and teachers, it is essential to determine the most efficient way to use it. Within the courses that employed e‐learning, Dermatology and Cardiology were selected as good examples to compare. These two courses have different features and cover different areas, but it was useful to use them for comparison as the functions used were similar and both were clinical subjects. The functions used were: uploading class material quizzes and class evaluation by students. In terms of function, the tasks were divided into two groups; compulsory tasks and voluntary tasks. Compulsory tasks included quizzes of Dermatology and Cardiology, and class evaluation of Cardiology. Voluntary tasks included material view of Dermatology and Cardiology, and

544

Hitomi Yukawa et al. class evaluation of Dermatology. In Cardiology the students were informed that the submission of quizzes and class evaluation affects final grades. The same was the case in Dermatology for quiz submission. In addition, the period of submission was limited from the time when all of the classes finished on the day to 9:00 the next morning. Students had no choice but to do the tasks. No difference was found among students. Nevertheless, it was almost impossible to identify poor performers during the term. On the other hand, voluntary tasks gave students freedom to do or not to do the tasks. In Dermatology, class slides were uploaded for all classes. In Cardiology, class materials were uploaded only 4 times (9%) after the class for confirming students’ knowledge. Interestingly, the view rate was almost the same between the courses. Students who did not tend to access the material showed lower grades on the term‐end examination. A benefit of e‐learning is that teachers can track students’ activities and identify students who do not perform sufficiently. To optimize e‐learning, teachers should track students’ performance from early on and find potential poor performers to support. In this limited study, we compared the student performance by grades and the type of tasks divided into compulsory and voluntary tasks. Compulsory tasks have the benefit that most students submit the tasks, but we cannot track how students submit them. Not all of the students might use their time effectively to learn from the tasks and might simply press the button to submit. So, quiz results of e‐learning may not be a good indicator to grasp students’ level of understanding. Though we have limited experience, it appears difficult to find efficient signs for identifying poor performers from the data of compulsory tasks. Voluntary tasks pose the risk that students may ignore the tasks. In other words, students have the choice to use the materials or not. In this study, students who tended not to access the voluntary materials showed lower grades on the term‐end examinations. This result suggests that voluntary task achievement is affected by actual student attitude to the course. Lower motivation for involvement in the course may result in lower grades. Achievement levels can be monitored during the term. As we have many ways to contact students through e‐learning, contacting students from early on can provide support for them, and at least can be a stimulation to study. Class evaluation questionnaires contain items that ask students’ level of understanding and interest on 5‐point scales. There were no significant differences in the calculated data between the groups in questionnaire scores divided by term‐end examination results. However, lower grade students tended to report lower levels of understanding and interest individually. This may be a slight but useful sign of poor understanding and interest from students. Therefore, it might be worth monitoring the results of class evaluation items as well to support students from early on,

5. Conclusion Categorization of tasks and comparison with grades may be feasible to explore more efficient ways for education and optimize the benefits of e‐learning. In this study, we compared achievement of compulsory and voluntary tasks with students’ final grades for the respective courses. Students completed compulsory tasks at higher rates though the results of achievement did not affect the final results. In contrast, students completed voluntary tasks at lower rates than compulsory tasks though the results of achievement showed a positive correlation with the final results. E‐learning produces a wealth of real‐time data. Utilizing this advantage, teachers should obtain the data of task achievement of voluntary tasks to support and stimulate potential poor performers from early on.

References Arianne M (2008) “A blended approach to active learning in a physiology laboratory‐based subject facilitated by an e‐ learning component”, Adv Phsiol Educ 32:65‐75 Delialioglu O (2004) “Investigation of source of motivation in a hybrid course” (on‐line) http://www.eric.ed.gov/PDFS/ED485032.pdf Kitsantas A and Chow A. (2007) “College students’ perceived threat and preference for seeking help in traditional, distributed, and distance learning environments”, Comp Educ 48: 383‐395 Ozuah, PO (2002) “Undergraduate medical education: Thoughts on future challenges”, BMC Medical Education Rowe, M. (2012) “Physiotherapy students' use of online technology as part of their learning practices: a case study”, S Afr J Physiother, 68(1): 29‐34 Ruiz, JG (2006) “The impact of e‐learning in medical education”, Acad Med, Vol.81, No.3:207‐212 Rusnakova, V. (2012) “Integration of the e‐learning into the medical university curricula”, Bratisl Med J Vol. 113, No.5: 324‐ 330 Shih CC and Gamon J (2001) “Web‐based learning: relationship among student motivation attitudes, learning styles, and achievement”, J Agricultural Educ 42: 12‐20

545

A Novel Approach to e‐Learning: Yasar University e‐Learning System (YES) Ibrahim Zincir, Melih Zeytinoglu, Ahmed Rana and Samsun Basarici Yasar University, Izmir, Turkey ibrahim.zincir@yasar.edu.tr melih.zeytinoglu@yasar.edu.tr ahmed.rana@yasar.edu.tr samsun.basarici@yasar.edu.tr Abstract: Since 2009, every student who attends Yasar University (Izmir, Turkey), regardless of their department, has to take part in seven courses (Human Sciences, Research Culture, Design Culture, Aesthetic Culture, Ethic Culture, Project Culture, and a Social Responsibilities Project) defined as University Foundation Courses (UFND). These courses are part of the university’s effort to graduate students who are not only hard‐working and respectable, but also who are responsible and beneficent to society and the environment that they are living in. Approximately 4000 students attend these courses every term, which in return results in certain challenges. One of the more pressing challenges is shortage of lecturers and classrooms, which is the root of a more visible problem; the complexity and difficulty of the timetable for the whole university. Hence, in January 2012, it was decided that a new approach was needed. At the start of the 2012‐2013 academic year, a novel E‐learning system was introduced to offer these seven courses online. Yasar University E‐Learning System (YES) differs from its peers in one important aspect: YES assesses the students continuously. YES does this by not only monitoring but also by encouraging them to get involved in the courses much more actively than any other system. In addition to the assessment of the students continuously, the new system evaluates the effectiveness of the users of the system by requiring them to attend the offered forums and live discussions with lecturers at least two times a week. This paper discusses the structure and the implementation of YES and the experiences so far. Furthermore, the paper draws some comparisons between YES and other popular E‐learning systems. Keywords: e‐learning, higher education, collaborative learning, linear learning

1. Introduction Learning is a lifelong process for human beings that help them to adapt to the different types of environments, which they are living in. Since the start of the information age, the rapidly changing technological development and economic conjuncture that is also partly connected to the technological development, results in similar changes and developments in education. These changes are not only concerned with the methodologies of education but also with the educational paradigms and approaches. The ease of access and the usage of technology create inequalities in the whole system and in the society. The technological divide is becoming more and more of a serious problem in the world, and is not only related to a specific country or a specific social level. E‐Learning brings a solution to this inequality and also delivers an opportunity, providing a life‐long learning scheme, which would contribute to the objectives of the education system while benefiting from the continuously evolving technological advances. Since the Internet provides modern and up to date information regardless of time and place, and lets the information to be shared by everyone openly, it presents a novel framework of learning tools for students. As a result, all around the world, the educational systems have to adapt to these constantly changing needs of the information age. The ancestor of E‐Learning; distance learning, originally started as a corresponding education system, and over time expanded to radio, television and telephone networks. Nowadays, however, distance learning, inherits online systems such as electronic mail, computer conferencing and multi‐media presentations over the Internet. These new tools create new opportunities for both educators and students and optimize distance learning as the information can be reached at any time and at any place. It should also be noted here that, while Smart et al stated that the potential aspects of E‐learning in higher education provides a great opportunity for both students and the teachers, they also mentioned that E‐learning is not suitable for students who do not have self‐discipline (Smart 2013, Wong 2007). As per classic definition, E‐learning “is teaching and planned learning in which teaching occurs in a different place from learning requiring communication through internet“ (Moore 2011). E‐Learning systems bring

546

Ibrahim Zincir et al. multiple options and provide enormous opportunities for students and educators since they do not require both parties to be present at the same place, i.e. on campus, all the time in order to present the learning material needed. Hence, educations all around the world aim to implement this approach into their structures, such as Open University of China with 2.7 million students, Indra Gandhi National University in India with more than 3 million students and Anadolu University in Turkey with 1.7 million students (Simpson 2013). Many researches have tried to improve learning skills of students. Polly et al claimed that E‐learning is essential in boosting critical thinking skills while Tamrakar et al developed a model to validate the efficiency of E‐learning via student interactions (Polly 2009, Tamrakar 2009). Wong presented that students with previous E‐learning experience are more successful than the ones who do not, while Bandeira stated that students with previous experience are much more found of online courses (Wong 2007, Bandeira 2009). Yasar University has also applied an E‐Learning system but in a novel way rather than the classical E‐learning methodologies and this paper discusses this novel approach.

2. Yasar University e‐Learning System (YES) In 2009, Yasar University’s Board of Trustees passed a resolution. This resolution requires every student who attends the university, regardless of their department, to take part in seven courses (Human Sciences, Research Culture, Design Culture, Aesthetic Culture, Ethic Culture, Project Culture, and a Social Responsibilities Project) defined as University Foundation Courses (UFND), Table 1. Table 1: University foundation courses

UFND 010

2 3 4 5 6 7

UFND 020 UFND 030 UFND 040 UFND 050 UFND 060 UFND 070

UFND 010‐A UFND 010‐B UFND 010‐C UFND 010‐D UFND 010‐E

Human Sciences Behavioral Sciences Semantic and Semiotic Philosophy and History of Science Technology and Society Philosophy ‐ Logic Research Culture Design Culture Aesthetic Culture Ethic Culture Project Design Social Responsibilities Project

These courses are compulsory for all Yasar University students. The main objective of these courses is to graduate students who are not only hard working and respectable experts in their specific areas, but also have a solid general knowledge and are responsible and beneficent to the society. More than 3800 students attend these courses every term and this number is increasing every year. Of course with the growing student numbers also come certain challenges. One of the more pressing challenges is the shortage of lecturers and classrooms. This problem is only the top of the iceberg and is the root of a more visible problem; the complexity and difficulty of scheduling classes and classrooms. In January 2012, the management of the university concluded that, as the growing number of students triggered the problems of having not enough venues and the lack of staff to conduct these courses, it was necessary to provide an efficient and alternative system to bridge the gap. As a result, a novel E‐learning system was proposed for these seven courses and in order to implement this system, a task force was established for each course. The purpose of these task forces were to develop efficient roadmaps to adopt and then deploy the courses online without compromising the quality and the features of the course contents while encouraging the participation of the students. Table 2 presents the schema of each task force. The whole system is coordinated by the project manager shown in the leftmost box. The manager is responsible for the coordination and management of all stakeholders and components of the system like the students, the teams but also the textbooks, videos, question bank etc. The second box from the left shows the working teams. There are three working teams, namely the academic team, the technical team and the law team. The definitions of the teams and their responsibilities are shown in the third rectangle. The academic team, which is the most important stakeholder of the system, is coarsely responsible for designing the learning content, training the instructors and training the students. It is their responsibility to write the textbooks, to prepare the presentations and to prepare the question bank. The technical team prepares the graphic design,

547

Ibrahim Zincir et al. the layout, and the media for the courses. Getting the data and the requirements from the academic team, this team is responsible for all working materials. They are also responsible for running the whole system from a technical point of view. They maintain the servers, develop the software and solve the technical problems that may arise. As the name implies the law team is responsible for all the legal issues of the whole project. In Table 3, the rightmost box shows the outcomes of the system. Those are the equipment, the learning materials in written, visual and auditory forms. Of course the learning portal is one of the main outcomes of the system too. After establishing the main structure of the system in the following 8 months, more than 10 textbooks, 100 video presentations and a question bank inheriting around 10000 multiple choice questions were prepared. Table 2: The structure of the Yasar University e‐Learning System (YES)

Table 3: Materials produced Academic Staff Product / Material 1 2 3

Books Videos Question Bank

Total 10 100

From Department (YES)

Lecturers from other Departments

Non‐University Lecturer

Non‐Academic Staff

10,000

Initially, virtual application modules were designed for every unit of each course. These modules consisted of book units, presentations and videos. After the completion of book units, multimedia videos were prepared. For each video of each unit, different scenarios were written so that the selected scenario in the end would demonstrate in a way that it enriches and summarizes the subject of the unit via significant real world examples. When needed, students also took part and participated in discussions with the lecturers at the multimedia presentations. During the preparation of all of these materials, visual attractiveness and resemblance to reality were taken into consideration. It should be noted here that, every one of these materials were prepared to be modified and upgraded if necessary. These courses as explained above, are Yasar University specific, hence the contents of the materials/books provided are unique. As a result, there is no single resource/book that can be found for each course explicitly and the books were written/the materials were prepared by a team of expert lecturers. For example, multiple choice questions prepared for each unit of the book was generated by at least 2 lecturers. As can be seen from all the requirements presented above, YES inherits a lot of aspects of E‐Learning and there are many features essential to the system. In order to implement an efficient E‐learning Online Software, Yasar University’s management gone through many readymade programs mentioned below in Table 4, which are also being used by various reputed higher education institutions all over the world, and concluded that the

548

Ibrahim Zincir et al. combination of its own software written by its staff members in addition to the Sakai E‐Learning program would meet the implementation requirements of YES the best. Table 4: E‐learning systems and universities Sakai Canvas Blackboard Desire2Learn Moodle CourseNetworking

Stanford University, Johns Hopkins University, Yale University, Columbia University University of Washington, University of Texas, Brown University, Southern Utah University University of Mississippi, University of Arkansas, Texas Tech University, Jacksonville University University of Arizona, Tennessee Tech University, San Jose State University, British Columbia Institute of Technology University of Georgia, University of Glasgow, Texas A&M University, University of Portsmouth Purdue University, Indiana University, Hoa Sen University, Gaza University

Although all of these existing E‐Learning systems let the students to access documents, multimedia files etc. at anytime and anywhere via the Internet, and monitor them continuously, none of these programs actually actively includes how the student interacts with the system (Stanford University, University of Mississippi, University of Arizona, University of Glasgow, and Brown University). The most important part of YES is, it encourages students to involve actively in discussions with the lecturers and other students, to fulfill the specific time requirements and enables them not to depend solely on the exam results.

3. Implementation It is a well‐known fact that any system that does not require the students to be at the same place with the lecturers has a disadvantage. That is the students have to be more responsible and be involved actively at the distance learning system. Hence, YES inherits a unique and novel approach that encourages students’ dedication to the course by implementing many unique features (i.e. the two tests only represents 20% of the final grade while forum participation at the right time and at the right place represents 40%). To this end, the 14 weeks long term is divided into two stages, 7 weeks each. The first stage forms 40% of the final grade, and the second stage forms 60% of the final grade. Each of these two parts consists of 5 different evaluation modules; chat, forum, reading material, multimedia, and test.

Chat: Depending on the lecture, 2 nights a week, the student has to chat with the other students and the lecturer during a one‐hour interval (i.e. between 20:00 to 21:00). This module makes up for the %10 of the grade.

Forum: Depending on the lecture, every week, the student has to initiate at least one forum topic and has to participate in at least two other topics initiated by others. This module makes up for the 40% of the grade.

Reading Material: Every week, students have to download the required reading materials, documents and presentations. These are only eligible to the students at the right time (defined by the lecturer) and each of these has to be read/downloaded only for a specific period of time. This module makes up for the 10% of the grade.

Multimedia: Students have to watch the required videos every week. Again these videos only appear on the system defined by to the unit at the appropriate time. This module makes up for the 20% of the grade.

Test: There are mock‐up tests that are generated via the test bank and can be taken anytime and anywhere by the students during the whole stage but the actual test has to be finished during the last week of the stage. This module makes up for the 20% of the grade.

Thus, it is not enough for a student just to write exams. The student is also required to participate actively during the online class discussions, chats and forums throughout the semester. This approach aims to overcome the disadvantage of not being in the same location with the lecturer. The grading of all activities is based on the idea of giving each student equal and comparable opportunity to learn using different skills, because each student learns differently. YES takes the approach that the student is the main factor in his/her own grade. He/she can choose in which part he/she is better and has to show higher performance to get a higher grade. The evaluation system is time dependent but the students can choose their own working hours within given time frames. Setting time limits for certain activities ensures that the students are not detached from the classes and lessons. This is meant as a psychological factor also to overcome the problem of having the feeling of being lost and alone.

549

Ibrahim Zincir et al. The chat and the forum modules help the students to understand the ideas better and also collaborate in learning. The most important module of the system is the forum module which makes up the 40% of the grade. The idea of this weight is to bring students from different departments together to talk about certain topics. In a world where multidisciplinary working and interdisciplinary dialog is getting more and more important, this approach helps the students to understand each other and to work together on different problems. Seeing different perspectives for a common topic helps the students to broaden their horizons and enrich their life experiences. This also prepares them better for their future work environments after their graduation from the university. From the beginning, it was intended to use a multimodal approach for YES. For now the system uses the well‐ known two input channels and one output channel for the students. The main mode used in the interaction with the system is of course the visual channel. This mode is supported by the auditory channel. As for the output channel, students have to use their haptic channel. All in all the system supports the main three channels of human‐computer interaction to enrich the interaction and to raise the experience. Besides the interactive, e‐learning approach, the university also established the academic counseling. The academic counseling is not meant to be understood only as a tutorial. In this context, there is a time schedule where the lecturers are physically in the classrooms to chat with the students and to answer their questions. Every student enrolled in the courses is free to meet at the announced days and times with the lecturer of the course. This offer is also used by the students extensively. As the academic counseling is not meant as a lesson there are many fruitful discussions during the aforementioned times.

4. Conclusion and future work Yasar University E‐Learning System (YES) was implemented at the start of the 2012‐2013 academic year for the Yasar University Foundation Courses. Approximately 3800 students attended these online courses and 14 lecturers gave a total of 50 classes each term. A year ago, only 2800 students were enrolled to these courses and there were 80 classes (which in return meant that there were 80 classrooms actively used) with 25 lecturers. Hence, this system is now supported not only by the university management but also by the students. It should be noted here that, although at the start of the fall term of 2012‐2013 of the academic year, the system was explained to the students thoroughly, still at the end of the term, it was seen that many of the students did not fully understand how the grading system was working and they thought that only by attending the two tests they would be able to pass the courses successfully. While we do not have the second term results yet, it can be easily seen from the system that all students this term has been involved in the lectures much more actively (more than 90% were at the chat rooms and forums every week, while it was only 50% at the first term), and the feedback from both the students and the lecturers is much more satisfactory during the second term. The multimodality of the system and the broad evaluation metrics of students’ works is one of the main reasons for the acceptance of the system by the students. Being free in their studies and not to be stuck in classrooms at given times is a beneficial working approach for most of the students. Not only learning and studying the given topic but also understanding and employing time management by themselves is both a challenge and an opportunity for the students. While students were concerned at the beginning because of the lack of classes, now they enjoy the freedom of being responsible for themselves as well as have the freedom to choose the time and place to attend these online classes. The understanding of the system has also been increased. As discussed earlier, the participation and the activeness of the students raised dramatically in the second semester. According to the lecturers and the logs of the forum and chats, the discussions in the groups are becoming more and more sophisticated. This can be seen as an indication that students get more and more used to the system and now concentrating more on the content rather than the system. As we stated above the latest numbers of the final grades of the students are not available yet but there is a tendency that shows higher results in the grades. One main benefit of the system can be easily seen although the remaining data is still missing. The decrease of the number of lecturers by increasing the students’ numbers gives the academic staff more freedom and opportunity to work on academic issues like research, writing papers/books etc. On the other hand, although the number of students is increasing, the support for

550

Ibrahim Zincir et al. the students becomes much better and stronger because the online system takes over many parts of the student evaluation process. From the management point of view, the system has solved one of the biggest problems for the university. That is the pressure on finding classrooms and lecturers to give all the aforementioned courses. The effectiveness of the system can be easily seen in the growing number of students and the decreasing pressure on scheduling challenges. All the above arguments indicate the advantages of the system, but they do not cover the content and the range of the system at all. Moreover, to assess the system better more statistical work should be done, too. All data including final grades, participation numbers etc. will be available to the authors after July 2013. There is also an ongoing interview process. The interviews include all participants of the system including the students, lecturers, technical and law teams. These interviews will show us the acceptance and the perceived advantages and disadvantages of the system. The authors believe that the best system is not good enough if the participants do not perceive it as a good system. After having such statistical studies as well as the evaluation of the system according to these studies and the interviews, the next step will be fine‐tuning the system to minimize the disadvantages, both evidential and perceived ones. Finally, it would be worthwhile to study how such experiences could be used in a broader range and include different topics in cooperation with even other institutions. The results will be published in the future works.

References Bandeira C. L. (2009) “Using E‐learning to Promote Critical Thinking in Politics”, Enhancing Learning in the Social Sciences, Vol. 1. Blackboard: http://www.blackboard.com/Sites/International/EMEA/index.htm (accessed 16/08/2013) Brown University: https://canvas.brown.edu/courses/150124/ (accessed 16/08/2013) Canvas: http://www.instructure.com/ (accessed 16/08/2013) CourseNetworking: http://www.coursenetworking.com/?from=page (accessed 16/08/2013) Desire2Learn: http://www.desire2learn.com/about/discover/ (accessed 16/08/2013) Moodle: https://moodle.org/about/ (accessed 16/08/2013) Moore M. and Kearsley G. (2013) Distance Education: A Systems View of Online Learning, Wadsworth Publishing, Belmont. Sakai: http://www.sakaiproject.org/ (accessed 16/08/2013) Simpson O. (2013) Supporting Students for Success in Online and Distance Education: Open and Flexible Learning, Routledge, New York. Smart K. L. and Cappel J. J. (2006) “Perception of Online Learning: A Comparative Study”, Journal of Information Technology Education, Vol. 5, pp. 201‐219. Stanford University: https://coursework.stanford.edu/portal/ (accessed 16/08/2013) University of Arizona: https://d2l.arizona.edu/ (accessed 16/08/2013) University of Glasgow: http://moodle.gla.ac.uk/ (accessed 16/08/2013) University of Mississippi: http://www.umc.edu/Education/Schools/Medicine/Graduate_Medical_Education/E‐ Learning.aspx (accessed 16/08/2013) University of Portsmouth: http://moodle.port.ac.uk/ (accessed 16/08/2013) Wong D. (2007) “A Critical Literature Review on E‐learning Limitations”, Journal for the Advancement of the Science, Vol. 2. Yasar E‐Learning System: http://e.yasar.edu.tr/ (accessed 16/08/2013)

551

552

PhD Research Papers

553

554

Cultural Differences in Students’ Perceptions Towards Online Learning Success Factors Armando Cortés and Elena Barbera Open University of Catalonia, Spain acorteso@uoc.edu ebarbera@uoc.edu Abstract: The purpose of this research was to identify factors of success in online learning from learners’ perception from four universities in different countries. A systemic and socio‐constructivist model of inputs‐process‐outputs of learning was used with five learner predictor factors (general self‐efficacy, self‐efficacy online, motivation, prior knowledge, and course expectation), eight institutional predictor factors (learner support, social presence, direct instruction, learning platform, instructor interaction, learner interaction, learning content, and course design) and three different outcome factors (learner satisfaction, knowledge acquisition, and knowledge transfer). A questionnaire was completed by 1119 learners. Significant differences are found from learners’ point of view in 14 factors and two factors indicated similarities: instructor interaction and learner satisfaction. Using the Hofstedes´ (2001) cultural dimension framework, this study examines differences and similarities between countries. The findings of this research will be helpful for faculty and instructional designers for implementing learning strategies addressing cultural differences. Keywords: critical success factors, learning satisfaction, knowledge acquisition, knowledge transfer, cross‐cultural, online learning, cultural differences, Hofstede

1. Introduction The impact of globalization in culture has been one of the most important issues in the educational world. This environment claims new competences for learners and instructors. A new task force is increasing with different characteristics of culture, age, gender, religion and language. The influence of Internet in business and education promote relationships between people around the word and migration has influenced countries with a diversity of cultures. All these new issues have built a new society. This research uses a systemic and socio‐constructivist model of inputs‐process‐outputs to identify factors that influence success of learning and for identifying differences and similarities. The cultural perspective for explaining this difference is the Hofstede’s (2001) cultural dimension framework. The article is organized as follows. The first section describes the model for identifying the selected success factors. It also covers a literature review on multicultural education and training and about cross‐cultural educational studies. Then a perspective of Hofstede is explained. The next section develops the research method that this research is based on. After this, results and discussion are found. Finally, conclusions are presented with recommendations for future research.

1.1 Purpose and research questions According to the literature review learners and institutional factors influence the outcome factors, and all of them have an influence in the learning process. For this reason, the purpose of this study was to analyse the relationship and differences between factors from different countries and to explain these differences and similarities in terms of culture. Two questions were addressed in this study:

Do online learners from the four countries perceive differences in learner institutional and outcome factors in their online learning courses?

How does the online learners’ culture impact their learning?

2. Theoretical framework 2.1 Systemic model There are different ways in which the success of the learning process has been measured. Some studies analysed the use of technology and the factors that enhance learning (Blignaut & Nagel, 2009). Shea and

555

Armando Cortés and Elena Barbera Bidjerano (2009) studies considered different types of online presence of learners. And Zhao (2003) compared success with the quality of the course, which resulted in the design of a framework based on evaluation. The systemic model used in this study provides a holistic picture of the teaching and learning process with a total of 16 interrelated factors, using five input variables that learners bring to the virtual classroom, eight process variables that the institution gives to experience a learning environment, and three outcome variables (satisfaction, knowledge acquisition and knowledge transfer; Barbera & Linder‐VanBerschot, 2011).

2.2 Perspective of culture in distance education Cultural issues have an impact on learner and institutional factors. For this reason, instructor and learner designers have to consider designing an e‐learning program. They have to take into account participants’ point of view, beliefs and values of learners from different cultures for implementing learning activities, assessment, feedback, interactions with the instructor and peers. The multicultural context is common in teaching and learning. Recently, immigration has increased and business and education institutions have been affected by the globalization and information and communication technology (ICT). Consequently, the mix of cultures, languages and cross‐cultural interaction increases and this situation claims new competences for employers, employees, learners and instructors. Despite the importance of developing skills to manage multicultural settings, there is not enough research to design courses that provide an environment that takes into account cultural differences. (Young, 2008), furthermore this study examines subjects in countries with strong changes in new population. In China, new technologies are emerging and university learners are exposed to the influence of western culture. In México, digital natives and the development of the infrastructure of telecommunications have influenced the exposition to multicultural society. In Spain, the prior exposition to technology, online education and the exposition to other cultures are changing the ways of education.

2.3 The Hofstede dimensions This study uses Hofstede’s cultural dimensions as the basis for the analysis and comparison of the cultural characteristics of learners from four countries: China, Mexico, Spain and USA. (Table 1). In the 1970s, Hofstede got access to a large survey about values and beliefs of people who worked at IBM in 50 countries around the world (Hofstede, 2001). Counting on this information, he was able to statistically distinguish cultural differences between countries and started to work on his future cultural dimensions. In the 2000s, five dimensions were developed: 1. Power Distance, related to the degree to which members of a society accept and expect that power is distributed unequally. A large power distance society accepts the inequity. 2. Uncertainty Avoidance, related to the degree to which a society feels the level of stress by unexpected situations. Societies with high uncertain avoidance ranking minimize the possibility of ambiguity situations. 3. Individualism versus Collectivism, related to the relationship between individuals and primary groups. People are more likely to integrate in countries with high collectivism ranking. 4. Masculinity versus Femininity, related to the distribution of values between the genders of a society. People are more assertive or competitive in countries with high Masculinity Ranking. 5. Long Term versus Short Term Orientation, which refers to the degree of focus for society level of effort in time. Hofstede´s Model has been criticized by different authors for the external validity of his work. This critics also consider that Hofstede’s dimensions are very basic and do not demonstrate the real national culture since the culture of a country has different cultures and characteristics by region (Jabri, 2005; Shattuck, 2005; Graen, 2006). Despite receiving some criticism, Wang (2007) used Hofstede’s dimensions and proposed them as a valid framework for investigating culture differences in other studies in education and several researches has used this framework for investigating intercultural interactions (Gudykunst, Chua & Gray, 1987; Olaniran & Stewart, 1996; Roach & Olaniran, 2001; Sanchez‐Franco, Martinez‐Lopez, & Martin‐Velicia, 2009). Table 1 presents the indexes of four nations from this study.

556

Armando Cortés and Elena Barbera Table 1: Country index scores of the cultural dimensions Dimension

Power Distance (PDI)

Individualism ‐ Masculinity ‐ Collectivism (IDV) Femininity (MAS)

Uncertainty Avoidance (UAI)

Long term orientation (LTO)

Spain

United States

China

118

Mexico

Source: Hofstede G. (2001). Cultures consequence: Comparing values, behaviours, institutions and organizations across nations. 2ed, SAGE, California. Table 1 can be explained as follows according to Hofstede (2001): Power Distance Learners and instructors from México and China have a large power distance index. This means that learners are dependent of instructors. In this case, learners show respect to instructors and education is in charge of instructors, who are viewed as gurus and their role is to transfer their knowledge. Normally, this group initiates communication in class, giving rules, information and following the tasks until they are finished. In contrast, Spain and USA have a small power distance index. This means that learners are treated as equals by instructors and learners can treat instructors as equals too. In this context, instructors are seemed as experts who transfer impersonal truths. Education is mainly focused on learners more than on instructors. Normally, learners start the communication in class. Individualism vs. Collectivism Mexico and China have a high index of collectivism. These societies believe that the idea of education is learning how to do things. Individual initiatives of learners are discouraged and collective initiatives are encouraged. Learners normally do not speak up in class; they only do when they are sanctioned by group. The environment in class is of organized learners working in groups. Also, degrees provide entry to a higher status group. In contrast, USA and Spain are more individualistic societies. Here, the purpose of education is learning how to learn. Also, individual initiatives or learners are encouraged and they expect to speak up in class for participating, sharing ideas or needs. Learners are organized according to their interests. Degrees increase what learners earn then, self‐respect increases too. Masculinity vs. Femininity USA, China and México have a high index of masculinity. Learners from these countries admire brilliant instructors. Normally, they treat to be the best learners and they are used to have competition in class. Instructors heap praises upon good learners, consequently learners over rate their own performance. Schools encourage competitive sports and promote wide participation as a part of the curriculum. Fail in school is a disaster for learners. Spain has a high index of femininity. Learners from this country like friendly instructors. Normally, learners treat to be in the average, hence, over‐ambition is unpopular. Instructors tend to give verbal feedback to weak learners. Here, learners tend to under‐rate their own performance. Schools promote competitive sports and wide participation out of the curriculum. Fail in school is a minor incident for learners.

557

Armando Cortés and Elena Barbera Uncertain Avoidance Spain and México have a strong uncertain avoidance. Learners from these countries want to know right answers and they demand them all. In this context, learners and instructors can express emotions in class. There is pressure among learners to be in or to be cast out. Normally, instructors can inform parents about their learners. China and USA have a weak uncertain avoidance. Learners from these countries want good discussions. In this context, instructors could say “I don’t know” and show the way to find answers. There is tolerance for differences in class. And normally, instructors involve parents in school activities. Long Term orientation China is a long‐term orientation country. Learners attribute success to effort and failure to lack of effort. Subsequently, studying hard is the norm; learners have high performance in mathematics and have talent form applied concrete sciences. Children learn to save. USA and Spain are short‐term orientation countries. Learners attribute success and failure to luck and occult forces. Enjoying school is the norm. Learners have low performance in mathematics; they have talent for theoretical abstract sciences though. Children learn to spend.

3. Method 3.1 Participating institutions and respondents The research includes four universities located in different countries: the University of New Mexico, the Open University of Catalonia, the University of Peking and the Autonomous Popular University of the State of Puebla in Mexico. Most of the courses were taught in the Education or Psychology Departments. These countries were selected since they are considered representative of their region. They have a great development of e‐learning institutions and the exposure of the learners to other cultures has grown. There were not found studies comparing these countries. Furthermore, findings will be useful in generating hypothesis and creating a profile of the online learner characteristics. Finally, contact with learners and university instructors were made by researches partners. Two surveys were used for data collection to know the perception of learners about the learner, institutional and outcome variables. The number of respondents in the first survey was 1119 and the number of respondents in the second was 707. No duplication was found in the collected data. The reasons of the drop out in the survey could be that some learners could have dropped the course too; other learners were probably motivated at the beginning of the course but miss motivation for answering the survey at the end of the course. The latter could be due to the amount of academic final tasks. Table 2 shows the number of respondents of each university. Table 2: Number of respondents by university Country

First Survey

Second Survey

UOC (Spain)

687

61%

380

54%

UNM (United States)

UPK (China)

177

16%

12%

UPAEP (Mexico)

198

18%

198

28%

Total

1119

100%

707

100%

3.2 Instrumentation This study adopted a systemic and socio‐constructivist instrument of inputs‐process‐outputs of learning created by Barbera and Linder‐VanBerschot (2011).

558

Armando Cortés and Elena Barbera The first survey included five factors with three items used for each one; in total 15 items were measured. Cronbach’s alpha was used to measure the reliability of the test survey and it resulted in .85 indicating high reliability. The second survey included five institutional factors and three outcome factors. Three items were used for each institutional factor with a total of 24 items, and five items were used for each outcome factor, with a total of 15 items. In total 39 items were measured. All items used a four‐point Likert‐type scale of potential responses: strongly agree, agree, disagree and strongly disagree. The Cronbach’s alpha was used to measure the reliability of the test survey and it resulted in .89, indicating high reliability.

3.3 Research procedure and data analysis Two online questionnaires to collect information were used. They were sent with accompanying consent forms. These were originally written in English and then translated by the researches to the official language of the country. The anonymous questionnaires were sent online to learners of the University using a web‐based data collection system. The first survey was sent at the beginning of the course and the second survey at the end of the course In order to analyse means and standard deviations SPSS 19.0 was used. An ANOVA was performed to investigate differences and similarities between countries. Although the number of responses was different at each university, the Central Limit Theory states that any sample size over 50 can be considered as normal. Thus, an ANOVA with unequal group sizes was run. A one‐way between‐subjects ANOVA was conducted to compare the perceptions of learner and institutional factors on the learning outcomes. As there were found statistically significant results in this example, there was need to compute a post hoc test and therefore selected the Games‐Howell post hoc test. This test is used with unequal variances and also takes into account unequal group sizes (Keppel & Wickens, 2004).

4. Results 4.1 Learner factors Table 3 shows that UNM learners had the highest composite scores in all five learner factors, whereas UOC students scored high in two factors, (self‐efficacy online, motivation). UPAEP students scored high in four factors (general self‐efficacy, Motivation, Self‐efficacy online, course expectation). PKU learners scored significantly high in one factor (self‐efficacy online). All five learner factors differed significantly according to the learners’ university. General self‐efficacy, self‐efficacy online and motivation were the top three most important factors that impacted e‐learning success in the four countries Learners from UPAEP and UNM agreed more with general self‐efficacy that could have a relationship with Hofstede’s dimension of masculinity and individualism, it was the most important factor according to UPAEP (M=3.31) learner’s perspective. UNM’s also agreed with this factor (M=3.25). The most important difference was in PKU learners and the other three universities (p= .000). Learners from UNM and UOC agreed more with self‐efficacy online. This is consistent with Kumar (2010), who found that individualism has a moderating role on the effect of the self‐efficacy abilities. United States has a high index of individualism (91) comparing with Spain (51), México (30) and China (20). There are differences in self‐efficacy online between PKU and UNM learners (p= .010) and between PKU and UOC (p= .019). There were no significant differences in self‐efficacy online between PKU and UPAEP learners (p= .992). Learners from UOC and UNM reported being motivated to learn in the course whereas PKU and UPAEP reported lower scores. This result may lead us to assume that individualistic societies are motivated by individually based needs and rewards (Hofstede 2001). There were significant differences among UOC, PKU

559

Armando Cortés and Elena Barbera and UPAEP learners’ perceptions, (p= .000) in motivation. There was an absence of difference between respondents of UOC and UNM. Prior knowledge had the lowest score from the perspective of three universities, UOC (M=2.74), PKU (M=2.81) and UPAEP (M=2.90). Course expectation was important for UPAEP (M=3.10) and UNM (M=3.02) learners; on the other hand, prior knowledge and course expectation had the lowest mean scores. UPAEP learners reported course expectation as a very important factor. In the Mexican society with a high PDI score, learners have high expectations in institution and instructors. Here, instructors are “gurus who transfer personal wisdom” (Hofstede, 2001, p. 107). Table 3: Mean and standard deviation for each learner factor from learner perspective: comparative results by country

UOC

UNM

PKU

UPAEP

N= 687

N= 57

N= 177

N= 198

Learner Factors

Mean

Sig

General Self‐ efficacy

3.01

0.46

3.25

0.57

3.03

0.67

3.31

0.57

.000

Self‐efficacy online

3.24

0.5

3.33

0.6

3.23

0.63

3.2

0.61

.000

Motivation

3.28

0.64

3.46

0.66

2.97

0.96

3.14

0.6

.000

Prior knowledge

2.74

0.51

3.06

0.51

2.81

0.71

2.9

0.64

.000

Course expectation

2.79

0.57

3.02

0.64

2.82

0.87

3.1

0.57

.000

4.2 Institutional factors Table 4 shows that UNM learners had the highest composite scores in all eight institutional factors, whereas UOC learners scored high in two factors (learner support, learner interaction). UPAEP learners scored high in two factors (learning support, learning interaction). PKU learners score high in two factors (social presence, learner interaction). Seven institutional factors differed significantly according to the respondents university. Learner support was the factor that had the highest score from the perspective of three universities UNM (M=3.58), UOC (M=3.21) and UPAEP (M=3.19). UNM learners agreed more with this factor. They agreed in the fact that they had enough access to resources and adequate training on the platform in order to be independent users of the platform. These findings echo Hofstede (2001), who says that learners tend to be independent using the platform, activities and assignments in low uncertainty avoidance countries (United States: UAI=46). The student at UNM agreed more with social presence and this was the university with the highest score (M=3.55). Likewise, learners from UOC (M=3.05), PKU (M=3.16) and UPAEP (M=3.01) reported similar perspectives. UNM learners reported a high score in instruction factor (M=3.59). There was a large difference between the latter university and PKU, which reported the lowest score (M=2.92). UOC (M=3.09) and UPAEP (M=3.08) had similar scores. Similar differences were reported in learning content and course design factors. UNM learners reported high scores in instruction factor. These findings echo Hofstede`s (2001) description of low PDI societies in School, where “the instructors are experts who transfer impersonal truths” (p. 107) and the relationship between instructors and learners is as equals. United States has the lowest PDI (40) score in universities of this study.

560

Armando Cortés and Elena Barbera UNM also reported high scores in learning content and course design factors. Learners believe that design of the course content has to be relevant. Material of the course has to be clear and should be encouraging. The latter echoes the low PDI where learners are independent and the systems need to be well developed to improve independence of learners. Table 4: Mean and standard deviation for each institutional factor from learner perspective: comparative results by country

UOC

UNM

PKU

UPAEP

N= 380

N= 42

N= 87

N= 198

Institutional Factors

Mean

Sig

Learner support

3,21

0,62

3,58

0,59

3,19

0,47

.000

Social presence

3,05

0,75

3,55

0,62

3,16

0,88

3,01

0,64

.000

Instruction

3,09

0,73

3,59

0,69

2,92

3,08

0,64

.000

Learning platform

3,06

0,66

3,32

0,72

3,01

0,87

3,06

0,46

.012

Instructor interaction

3,05

0,78

3,19

0,96

2,93

2,99

0,7

.281

Learner interaction

3,15

0,66

3,38

0,72

3,15

0,86

3,16

0,53

.016

Learning content

3,09

0,07

3,59

0,26

3,02

0,96

3,1

0,59

.000

Course design

3,09

0,14

3,52

0,71

3,02

0,95

3,1

0,55

.000

4.3 Outcome factors As shows in table 5, two outcome factors differed significantly according to the respondents’ university: knowledge acquisition and ability to transfer. PKU learners were the only ones with low scores in learner satisfaction (M=2.80) and knowledge acquisition (2.92). In both cases, scores were under 3. UNM learners reported high scores in ability to transfer. The other universities agreed with similar scores: UOC M=3; PKU M=2.97 and UPAEP M=3.10. Table 5: Mean and standard deviation for each outcome factor from learner perspective: comparative results by country

UOC

UNM

PKU

UPAEP

N= 380

N= 42

N= 87

N= 198

Outcome Factors

Mean

Sig

Learner satisfaction

3,23

0,67

3,46

0,47

2,8

1,1

3,3

0,57

.032

Knowledge acquisition

3,11

0,68

3,42

0,7

2,92

1,03

3,1

0,57

.006

Ability to transfer

0,7

3,44

0,72

2,97

1,08

3,1

0,58

.000

5. Discussions and implications The purpose of this study was to analyse the most important factors for success in online learning from the point of view of learners and investigate possible cultural causes to explain the differences and similarities. The results of this study revealed significant differences among learners in 14 of the 16 factors, in general the highest rated learner factor was self‐efficacy online, the highest rated institutional factor was learner support and the highest rated outcome factor was learner satisfaction, the lowest rated learner factor was prior knowledge, the lowest rated institutional factor was instructor interaction and the lowest rated outcome factor was ability to transfer. Instructor interaction was one of the factors without significant differences. Mean scores for this factor are similar in UPAEP, PKU and UOC. As for UNM learners, it was slightly higher.

561

Armando Cortés and Elena Barbera Instructor interaction has been studied and has a factor that significantly influences learning outcomes: learner satisfaction (Artino, 2007; Eom, Wen and Ashill, 2006; Selim, 2007), knowledge acquisition (Mayer, 2002), and knowledge transfer, (Holton, 2005; Yamnill & McLean, 2001), furthermore, research studies have indicated that uncertainty avoidance has negatively influenced communication practices of instructors with learners, this findings echo Hofstede because Spain and Mexico are countries with high index of uncertainty avoidance and satisfaction with communication practice is low. In this study, UPK learners scored lower in instructor interaction and UNM learners scored quite high. According to literature, the reason why the differences are not higher is that learners from China have more exposure to technology and their interaction with instructors and peers has been influenced by western cultures. Another factor without significant difference was learner satisfaction, with high scores in three universities, UOC, UNM, and UPAEP, and slightly lower scores as for PKU. According to Hofstede (2011), collectivist cultures have a strong association with customs and traditional methods. It is not easy for these societies to accept changes in education methods, and education; it is a medium for upward social mobility and making relationships in the society. They prefer face‐to‐face interaction with the instructor. México and China score as collectivist cultures. In contrast, in individualistic societies, learners and instructors accept changes easily and they prefer to learn using technology and normally are satisfied with online learning. The UNM and UOC scored high for both individualism and satisfaction. The findings here echo the abovementioned study.

6. Conclusions, limitations and future research This study indicated that significant factors from learners’ perceptions echo Hofstedes´ (2001) cultural dimension framework. However, there are some issues to take in account in this educational setting:

Two primary factors that learners believe to be the most important in establishing an effective online classroom were online self‐efficacy and learner support, the highest rated institutional factor.

Online learners could be different from a typical learner from the same country. The exposure to technology, interactions and expectations are different.

Instructor interaction is important for all four countries. This factor doesn’t depend on the culture they belong to.

There are some differences in Chinese learners and this could be caused by the globalization of the economy in China and the western influence.

It is essential to know the significant effect of culture in online leaners in order to design courses that take into account the multicultural environment. Instructional designers and instructors could design activities that develop relationships between learners and learn about the cultures of their peers. The university management has to be involved in the process attending differences of multicultural groups and promoting academic staff activities for acquiring better intercultural awareness. Online instructors should count on appropriate cross‐cultural training in order to develop their intercultural competences. In order to carry out an efficient communication with learners of different cultures, instructors need to have a good exposure in both, online and on campus courses, either formally or informally. The findings of this study show that instructors need to use different activities that permit integration and communication with learners from cultures with low and high indexes in Hofstede dimensions.

6.1 Limitations and future research This study had some limitations. The research was made only with a sample of online learners; next studies could make a research including the point of view of academic staff, instructors and managers. On the other hand, the implication of learners from social science departments was strong; this could be biased because the items and terms in surveys were well known for them. For this reason, next studies could be carried out in other university departments and could include samples from other countries.

562

Armando Cortés and Elena Barbera A further limitation is that the Hofstede’s work we used to explain cultural differences was made with a sample of employees of an international company, and that could be a subculture and not the dominant culture of the country. This weakness in the work of Hofstede was indicated by Marcus (2000) and this study was performed in universities, which could also be considered as a subculture of each country. For these reasons, future research could take into account analysis of subcultures, such as gender, age, and prior exposure to other cultures.

References Artino, A. R. (2007). Online military training: Using a social cognitive view of motivation and self‐regulation to understand students’ satisfaction, perceived learning, and choice. Quarterly Review of Distance Education, Vol. 8, No. 3, pp 191‐ 202. Barbera, E., and Linder‐VanBerschot, J. A. (2011). Systemic multicultural model for online education: Tracing connections among learner inputs, instructional processes and outcomes. Quarterly Review of Distance Education, Fall, Vol. 12 No. 3, pp 167‐180 Blignaut, S. and Nagel, L. (2009). Cousins Virtual Jane and Virtual Joe, Extraordinary Virtual Helpers. Computers & Education, Vol. 53, No. 1, pp 104‐111. Eom, S. B., Wen, H. J., and Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, Vol. 4, No. 2, pp 215‐235. Friedman, B. A. (2007). Globalization implications for human resource management roles. Employee Responsibilities and Rights Journal, Vol. 19, No. 3, pp 157‐171. Graen, G. B. (2006). In the eye of the beholder: Cross‐cultural lesson in leadership from Project GLOBE. Academy of Management Perspectives, Vol 20, No.4, pp. 95–101. Gudykunst, W. B., Chua, E., and Gray, A. J. (1987). Cultural dissimilarities and uncertainty reduction processes. In M. McLaughlin (Ed.), Communication Yearbook, Vol. 10, pp. 457‐469. Sage: California. Hofstede, G. (1991). Cultures and organizations: Software of the mind: Intercultural cooperation and its importance for survival, McGraw Hill: New York. Hofstede G. (2001). Cultures consequences: International differences in work‐related values. Sage: California. Hofstede G. (2001b). Cultures consequence: Comparing values, behaviors, institutions and organizations across nations. 2ed, Sage: California. Hofstede, G.J. (2009) ‘Research on cultures: how to use it in training?’, European J. Cross‐Cultural Competence and Management, Vol. 1, No. 1, pp.14–21. Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede Model in Context. Online Readings in Psychology and Culture, Vol. 2, No. 1. [online], http://dx.doi.org/10.9707/2307‐0919.1014 Holton, E. F. III. (2005). Holton’s evaluation model: New evidence and construct elaborations. Advances in Developing Human Resources, Vol. 7, No. 1, pp 37‐54. Jabri M. M. (2005). Commentaries and Critical Articles : Text–context Relationships and Their Implications for Cross Cultural Management. International Journal of Cross Cultural Management, Vol. 5, No. 3, pp 349‐360. Keppel, G., and Wickens, T.D. (2004). Design and analysis: A researchers handbook (4rd Edition). Upper Saddle River, Pearson: New Jersey. Kumar, R. and Uzkurt, C. (2010) Investigating the effects of self‐efficacy on innovativeness and the moderating impact of cultural dimensions. Journal of International Business and Cultural Studies. Vol. 4. No. 1. Lim, H., Lee, S‐G. and Nam, K. (2007), “Validating E‐learning factors affecting training effectiveness”, International Journal of Information Management, Vol. 27, No. 1, pp. 22‐35. Marcus, A, (2000). "International and Intercultural User‐Interface Design," in Stephanidis, Constantine, ed., User Interfaces for All, Lawrence Erlbaum: New York. Mayer, R. E. (2002). Rote versus meaningful learning. Theory into Practice, Vol. 41, No. 4, pp 226‐232 Nisbett, R. E., Peng, K., Choi, I., and Norenzayan, A (2001) Culture and systems of thought: Holistic versus analytic cognition. Psychological Review, Vol. 108, No. 2, pp. 291‐310. Nisbett, R. E., and Masuda, T. (2003). Culture and point of view. Proceedings of the National Academy of Sciences of the United States of America, 100, 11163‐11175. Nisbett, R.E. (2003). The geography of thought: How Asians and westerners think differently. And why. Free Press: New York. Olaniran, B. A., and Stewart, R. A. (1996). Instructional Practices and classroom communication apprehension: A cultural explanation. Communication Reports, Vol. 9, No. 1 pp 193‐203 Roach, K. D., and Olaniran, B. A. (2001). Intercultural willingness to communicate and communication anxiety in International Teaching Assistants. Communication Research Reports, Vol.18, No. 1, pp 26‐35. Sanchez‐Franco, M. J., Martinez‐Lopez, F. J., and Martin‐Velicia, F. A. (2009). Exploring the impact of individualism and uncertainty avoidance in web‐based electronic learning: An empirical analysis in European higher education. Computers & Education, Vol. 52, No. 3, pp 588‐598. Selim, H. M. ( 2007). Critical success factors for e‐learning acceptance: Confirmatory factor models. Computers & Education, Vol. 49, No. 2, pp. 396–413

563

Armando Cortés and Elena Barbera Shattuck, K. (2005). Glimpse of the global coral gardens: Insights of international adult learners on the interactions of cultures in online distance education. Unpublished doctoral dissertation, The Pennsylvania State University. Shea, P., and Bidjerano, T. (2009). Cognitive presence and online learner engagement: A cluster analysis of the community of inquiry framework. Journal of Computing in Higher Education, Vol. 21, No.1, pp 199–217. Wang, M. (2007). Designing online courses that effectively engage learners from diverse cultural backgrounds. British Journal of Educational Technology, Vol. 38, No. 2, pp 294–311. Yamnill, S. and McLean, G. N. (2001). Theories supporting transfer of training. Human Resource Development Quarterly, Vol. 12, No. 2, pp 195‐208. Young, P.A. (2008). The culture based model: Constructing a model of culture. Educational Technology & Society, Vol. 11, No. 2, pp 107‐118. Zhao, F. (2003). Enhancing the quality of online higher education through measurement . Quality Assurance in Education. Vol. 11, No. 4, pp 214‐221.

564

Visual Analytics by Animations in Higher Education Jan Géryk CVT and KD Lab Faculty of Informatics, Masaryk University, Brno, Czech Republic xgeryk@fi.muni.cz Abstract: In this paper, we focus on educational data analysis supported by animations. Many analytical tools employ visual techniques to facilitate the analytical process using both static visualisations and support animations in some way. Moreover, the application programming interface of many standalone applications, JavaScript libraries, and statistics and analytics software packages enable analysts to combine their functionality with conventional data mining and machine learning methods, e.g. Google Charts Tools, GGobi or iPlots. Visual analytics (VA) has a wide area of applications in higher education. We are developing an analytical tool with visualisation and animations support. The main goal of the tool is to contribute to an increase in education efficiency and quality by means of novel data visualisation techniques which enable better exploratory and interactive analysis. We describe two projects that take advantage of visualisation of educational data gained from the database of the Information System of Masaryk University (IS MU). The first project is concerned with the design of the preliminary version of the VA tool. Particularly, we focus on the combination of visual techniques and animations that support exploratory analysis of educational data. We present two visualisation techniques in detail, namely motion charts and an extended version of motion charts which is more suitable for analysing of educational data. In the second project, we demonstrate the use of Visual Analytics for generation of new attributes from a social network of student communication. We also describe potential tasks of Visual Analytics in university information systems. Keywords: motion charts, visual analytics, animations, student drop‐out, social behaviour of students, attribute generation

1. Introduction One of the significant trends in higher education is the overwhelming growth of educational data. Furthermore, the problem of information overload increases the danger of getting lost in data. Therefore, it is inevitable to develop methods and models to extract reliable and comprehensive knowledge. Han et al (2011) show that sophisticated data analysis approaches such as statistics or data mining (DM) were developed independently of visualization techniques. However, some key ideas influenced the scope of the fields. It resulted into what is nowadays called Visual Analytics (VA). One of the most significant reasons was the need to move from confirmatory data analysis to exploratory data analysis. Examples of such analysis can be found in Theus et al (2011). Generally, visualization techniques significantly help analysts to identify trends in the data. How to choose the right kind of visualisation method for different purposes can be found in Fry (2008). Nevertheless, it is often of great interest to reveal concealed changes in multivariate time series of data. Therefore, novel visual techniques are needed to highlight important features of the data and also to filter out irrelevant information, as Ware (2004) states. Animations, for example motion charts, represent one of the possible solutions for analysing multivariate time series of data. Hans Rosling firstly introduced motion charts in his tool Gapminder in 2011 (http://www.gapminder.org/). The basic display consists of a 2D bubble chart showing the variables x and y that have been recorded regularly for inspected objects. Changes over time can be visualized by highlighting the bubble positions. Additional information about the inspected objects can be kept in the colours of the bubbles or their size. On the other side, some analysts, for example Robertson et al (2008), emphasize that animations are not always better than static visualisations. The main focus of higher education institutions is to improve the quality and effectiveness of education. Romero et al (2007) state, that the goals can be achieved using educational data mining (EDM). It is related to several research areas, including e‐learning, adaptive hypermedia, intelligent tutoring systems, web mining, DM or visualization. Delavari et al (2008) present that the application of DM techniques in higher education systems have some specific requirements, not present in other areas. Our university has a long term interest in analysing educational data stored in the university information system (IS MU) in order to decrease early drop‐outs and make studies more efficient. It is closely connected with changes of mode within the same field of study and changes of the field of study. IS MU stores data comprising of all information about students, their studies, teachers, courses and the administration of the university. It also provides e‐learning materials. Moreover, the database contains the whole history of every web request.

565

Jan Géryk In the next chapter, we present related success stories of VA in support of higher education. Subsequently, we briefly introduce existing tools and software packages suitable for data visualisation and animation techniques, especially on the web. These can be beneficially utilized in VA to support conventional DM methods. Then, we describe two projects concerned with visualisations of educational data gained from the database of the Information System of Masaryk University (IS MU) (http://is.muni.cz) that is designed to support the administration of studies and e‐learning concepts. Then, we present potential tasks for VA in support of university information systems. Finally, we conclude the paper with conclusion.

2. Success stories using visualisations Sirisack et al (2011) present a successful employment of a two‐stage method for making animated bubble charts in Excel. In the first stage, a macro written in Visual Basic for Applications (VBA) helps to identify data tables in a given worksheet and the macro also creates a suitable bubble‐chart template. Thereafter, a collection of other macros enables to produce the final animation. They designed methods to process large datasets with multiple groups of objects and multiple observations in time. The paper is concluded with the following findings. Animations can save time by producing a good overview of complex datasets. By highlighting a subset of bubbles analysts can get a wider perspective. Finally, the shape and size of a highlighted subset can be inspected while the previous states are still remembered. Arnold (2010) introduces a successful utilization of VA in the early academic alert system called Signals. The system was developed by a research team at Purdue University and implemented into the Blackboard Learning Management System. Students are intuitively assessed by three lights of different colours namely green, yellow and red. The colour indicates the quality of factors related to study success when compared with the other students. The evaluation algorithm considers both the academic performance data and the evidence of student effort. A similar alert system utilizing colours to indicate the importance or problem has been also employed in IS MU. However, it reminds important events. Jin et al (2009) present a combination of visualisation with conventional DM methods that were applied at Normal University in Wuhan. A visual data mining model was designed and subsequently implemented in their higher education evaluation system. They concluded that VA can be useful in discovering valuable knowledge and the informative characteristics. Popelínský et al (2008) presented DZEMUj, a system for data mining in e‐learning data, namely in electronic tests. DZEMUj extends capabilities of the e‐learning module of IS MU and offers a rich set of tools for visual data analysis. DZEMUj enables to identify the most difficult and the ambiguous test queries, to discover associations among answers and also corresponding behaviour of students. Four visualisation techniques are available for VA ‐ scatter plots, decision trees, frequent patterns and RadViz. Extended version was introduced by Nosál (2013). Hilpert (2011) present use of motion charts with diachronic corpus data to visualize dynamic language changes. The Inputs for motion charts are constituted of bivariate and multivariate data sets. They successfully utilize the charts to illustrate recent changes in American English. The charts show series of diachronically ordered scatter plots that can be viewed in sequence. They verify that even if motion charts are typically used to represent bivariate data sets, they are also useful for the analysis of multivariate data over time. Al‐Aziz et al (2010) have developed Java‐based infrastructure, SOCR Motion Charts, for exploratory analysis of multivariate data. The tool enables the visualisation of high‐dimensional longitudinal data. It allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colours, glyphs and appearance characteristics. They also validated this visualization paradigm using several publicly available multivariate datasets.

3. Data visualisation tools with animation capabilities Increasing computing power and accelerating research progress in areas such as VA or information visualisation stimulate the development of new visual applications. The software package XGobi and its successor GGobi enabled interactive modification of multiple linked plotting windows. Rich application programming interface (API) facilitates such methods to users of the R

566

Jan Géryk statistical environment, as Cook et al (2007) describe. Zooming and scaling were established as standard functions in software for time series analysis, and visual specification of queries was introduced to reveal interesting features of time series data by Hochheiser et al (2003). iPlots is another well known package for the R. It covers similar visual functionality as the previously mentioned package – GGobi. Motion charts were firstly introduced in Gapminder. Among others, it represents a great progress in data visualization for analysis of multivariate time series of data. Apart from the source code, The Google Charts Tool has made motion charts freely available for all users. Fortunately, developers can utilize the provided API. Protovis and its successor D3, as Murray (2013) describes, set a new standard for creating visualisations and animations on the web. The framework is based on a set of new web standards including CSS3, HTML5 and SVG. Main advantage is the combination of visualization components and the data‐driven approach with the Document Object Model manipulation.

4. Visual analytics projects in IS MU 4.1 Motion charts The first project deals with development of an analytical tool with visualisation support and animation capabilities. The tool is specialized on exploratory and interactive analysis of educational data. A great attention is paid to multidimensional data analysis. The main drawback of the static visualisation is the risk that the data representation can be visually unintelligible. Moreover, multiple views of static visualisations can be devalued by visual fragmentation and perceptual interference between different graphical codes, as Bartram (2001) states. The motion charts, described in Battista et al (2011), are designed to overcome both the mentioned perception issues. Apart from that, the animation has the ability to attract attention to objects moving simultaneously in the same direction and to detect outliers that move in completely different directions. Visualisation of the whole data set using bubbles enables to identify clusters easily. Motion charts and animated bubble charts, described in Sirisack et al (2011), have same principle. The bubble chart shows data using the x and y axes, and the size and colour of the bubble. A motion chart displays changes over time by showing the movement within the two‐dimensional space and changes in the size and the colour of the bubbles. This advanced visualisation methods show more interesting stories in the data, as Battista et al (2011) show. They also show high level patterns as well as the individual elements that make up pattern. Motion charts enable to perform both high‐level analysis (identifying long‐term trends) and targeted analysis (impact of a specific change or event). Motion charts ask a lot of analysts' visual pattern recognition skills. Bubbles are floating around in all directions, changing sizes and colours. The motion charts enhance the detection of data changes using perception of the size or colour of the bubbles. On the other hand, only a few different colours and sizes can be easily distinguished by visual inspection. The design of motion charts and interface of data selection should be flexible, especially the control of time dimension is essential. Gilmore (1989) states that the threshold limit value is eight different colours for static bubble charts and four colours for animated charts. The choice of bubble size is crucial to the quality of perception of a pattern formed by a set of bubbles. Too small bubbles have a tendency to blur the contours of bubble clusters. On the other hand, too large ones make the perception of the number of bubbles in different clusters more difficult.

4.2 Motion charts for student migration analysis Motion charts is one of the most illustrative and clear mechanism how to simultaneously present several student attributes changing in time, e.g. the study progress. It overcomes related issues of the list of nominal aggregated values or static graphs. Nominal values can be too complex and static graphs do not express the changes in time. Motion charts allow us to clearly depict very complex relations in a very simple way. The motion charts are designed to present five variables including the time dimension variable. The rest are as follows:

Second and third mandatory variable represent the position (in x and y axes).

The fourth variable is optional and is expressed as the bubble colour. It can be used to visually separate the bubbles into different subsets.

The last variable is coded as the size of bubbles and is also optional.

567

Jan Géryk The Fig. 1 illustrates example of the motion charts used in the study progress analysis. All the bubble chart attributes are related to the students conducted to study in the corresponding fields of study. Values on x‐axis depict the average grades. Values on y‐axis depict the the average number of gained credits. The time dimension corresponds to the progress of the study and is expressed with the animation. The bubble size corresponds to the number of students in particular field of study. The bubble colour represents the following additional study attributes:

blue–students of the bachelor’s programme Informatics

orange–students of the bachelor’s programme Applied Informatics

The implementation of motion charts in IS MU utilizes D3 JavaScript library. The data can be got directly from the database or can be loaded in data format JavaScript Object Notation (JSON). JSON is derived from the JavaScript scripting language and is based on the concepts of XML. It is designed to represent simple data structures and associative arrays. JSON has a strong support in many programming languages. The graphical user interface (GUI) of the tool is built on the web based framework of IS MU. We put emphasis on the simple control settings and maximizing the utilization of the screen. If you hover over a bubble, you can see the exact value. If you hover over the number, you will stop the animation and you can change the time stamp. Other software packages or libraries can provide different solutions to animation problems. In R statistical environment, sequences of frames representing different time stamps are combined into a video prior to the animation. The Google Chart Tools can provide several types of interactions as it creates the animation in the web browser in real time. The flexibility and the ability to manage animations of large data sets are the main technical advantage of our implementation. We had to optimize animation process, otherwise even hundreds of bubbles significantly reduce the speed and distract analysts. Therefore, we are able to manage thousands of animations simultaneously. Moreover, we have interface to database of IS MU so we can easily load data for analysis.

4.3 Extended version of motion charts We have also developed an extended version of motion charts more suitable for educational data analysis. The motivation to create the extended version was to enhance its expression capability enabling us to depict each student that are central objects of our interest. Therefore, the large bubbles representing logical group of students are consisted of small bubbles that represent students individually. The modified concept of motion charts also enables to display groups of students and their transitions between the groups. Unlike the fundamental concept of motion charts, the extended version supports several types of animations performed simultaneously. The implementation enhances the portfolio of animations which express the student behaviour more accurately. Besides from the study progress, animations are also employed to express the study termination, the change of mode within the same field of study and the change of field of study. The Fig. 2 illustrates example of the extended version of motion charts used in the study progress analysis. All the bubble chart attributes are related to the students conducted to study in the corresponding fields of study. Values on x‐axis depict the average grades. Values on y‐axis depict the the average number of gained credits. The time dimension corresponds to the progress of the study and is expressed with the animation. The bubble size corresponds to the number of gained credits. Each of the group of small bubbles corresponds to a particular field of study. Drop‐out students turn red and fall down in the semester when they finished. The stroke‐width of the bubbles represents the state of the study and the bubble colour represents the following additional study attributes:

blue–students of the bachelor’s programme Informatics

orange–students of the bachelor’s programme Applied Informatics

red–drop‐out students

yellow–students who have changed the field of the study

green–students who have changed the mode in the same study

568

Jan Géryk

Figure 1: Motion charts application

Figure 2: Extended motion charts application

4.4 Visual analytics of social network In the second project, Bayer et al (2012) focused on predicting drop‐outs and school failures when conventional educational data has been enriched with data derived from students’ social behaviour. The data describes social relations gathered mainly from statistics of e‐mail and discussion board conversations. In the paper, we introduced a novel method for learning a classifier for student failure prediction in the early stages of the study. Firstly, we selected important attributes about students and their studies such as the gender, the year of the birth, the number of finished semesters, recognized semesters, gained credits, credits to gain, the average grade or the weighted average grade. Secondly, we applied machine learning methods from Weka on the educational data and build a classifier predicting the student failure with the accuracy about 80 %.

569

Jan Géryk The crucial part of the project consists in VA of the social network we constructed from the data about student social behaviour. The most important interpersonal ties we have took into account are the following: mutual e‐mail conversations, publication co‐authoring, discussion board messages marked as important or direct comment on another person. To obtain knowledge concerning a student from perspective of his or her engagement in the school community, we constructed a sociogram. It is a diagram mapping the structure of interpersonal relations. The result gained using Kamada‐Kawai energy layout algorithm can be seen in the Fig. 3. The dark nodes represent students that successfully finished their studies, whereas the white nodes represent the unsuccessful students. Such diagram allows us to identify new features by link‐based ranking.

Figure 3: Network with vertices arranged by Kamada‐Kawai energy layout algorithm This single mode social network of students and their interpersonal ties (i.e. homogeneous information network) allows us to employ specialized tools for social network analysis, e.g. Pajek. Using this tool, we constructed new features from the network, e.g. degree, indegree, outdegree, the sum of incident line values or betweenness centrality. We selected four student features from the educational data set according to their information gain, to calculate averages of the neighbourhood values–capacity‐to‐study test score, average grades, proportion of enrolled to gained credits and the number of credits per semester. Using the network analysis we computed new attributes and enriched the conventional educational data. It resulted in a significant increase of the accuracy of the learned classifiers that predicted the student success of failure with the accuracy higher than 93 %. The most successful classifier was based on the C4.5 algorithm generating rules–PART.

5. Potentials of visual analytics in university information systems 5.1 Heterogeneous network of courses University information systems (UIS) usually contain metadata about every course conducted at the university. Informative features can be selected from the metadata using VA and feature selection (FS) methods. Courses can be visualized using a heterogeneous network based on the selected features. Analysts can discover concealed relations and thus contribute to improving the overall course structure. Naturally, it will help students to select courses in a more effective way. Similar approach can be utilized for identification of courses with overlapping contents. It enables teachers to redesign and optimize courses.

5.2 Mapping online discussions The relevancy and the quality of each post can be assessed by both the structural characteristics of threads and users’ assessments. Representing network of threads graphically will facilitate identification of posts breaking discussion rules. Subsequently, administrators can easily disable access for authors of such posts. Networks also simplify the identification of discussion leaders and help teachers to assess students according to the interactions in group discussions.

570

Jan Géryk

5.3 Social network visualisation Social relations are created by staff members and students using UIS. Besides the visible interaction, e.g. email communication, the latent interactions ‐ described by Jiang et al (2010), e.g. profile visiting, also contribute to construct social networks. VA of the networks facilitates deeper understanding of user interactions. Consequentially, it enables developers to redesign social platforms and better personalized applications. Similar approach can be utilized to determine the most significant properties characterizing students and to predict student performance.

5.4 Visualization of recommender system Sacín et al (2009) state, that a recommender system is a filtering system producing lists of recommended tasks. Such a system should be helpful for students to personalize their study plans and to propose suitable study materials. The system can utilize a combination of two main approaches: the collaborative and the content‐based filtering. Naturally, the recommendations can be properly visualized.

6. Conclusion VA has a wide area of applications in higher education. Visualisation techniques mainly facilitate the process of the exploratory and interactive analysis. Analysts often need to employ visualization techniques helping them to identify any type of trends in the data. We described two projects dealing with educational data visualisation. However, static visualisations suffer from the risk that the data representation will be visually unintelligible. Therefore, novel visualisation techniques are needed to highlight important features of the data and also filter out irrelevant information. Animations represent one of the possible solutions to overcome the issues. Apart from that, the animations have the ability to attract attention to objects moving simultaneously in the same direction and to detect outliers that move in completely different directions. A novel tool with animation and visualisation support was introduced. The tool is specialized on exploratory and interactive analysis of educational data. Additionally, general potential tasks for VA in higher education were described. Colleagues and I concentrate our effort on early drop‐out prediction and related issues. The analysis of reasons of change of mode within the same field of study or change of the field of study is worth mentioning. We used to utilize only conventional data mining methods on data from IS MU. In order to improve our results, we attempt to combine data mining methods with visualisations. We utilize visualisation techniques in the process of feature extraction and feature generation too. The challenge is to develop novel interactive visualisation methods that enable exploratory analysis and to reveal interesting trends in the educational data. In this paper we have presented two visualisation methods with animation support. Both of them are based on motion charts. The first one enhances the charts with additional functionality, e.g. trails. The other one constitutes a novel method that extends the fundamental concept of the motion charts. Moreover, the methods help us to extend our background knowledge and to identify attributes useful for data mining methods. The methods were successfully applied on the educational data from IS MU.

Acknowledgement I thank Michal Brandejs and all colleagues of IS MU development team and Knowledge Discovery Lab for their assistance. I also thank Lubomír Popelínský for his help. This work has been partially supported by Faculty of Informatics, Masaryk University.

References Al‐Aziz, J. and Christou, N. and Dinov, D. I. (2010) SOCR Motion Charts: An Efficient, Open‐Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data. In JSE Proceedings, Volume 18, Number 3, Statistics Education, pp. 1‐29. Arnold, K. E. (2010) Signals: Applying academic analytics. EDUCAUSE Quarterly. Bartram, L. R. (2001) Enhancing Information Visualization with Motion. Simon Fraser University.

571

Jan Géryk Battista, V. and Cheng, E. (2011) Motion Charts: Telling Stories with Statistics. In JSM Proceedings, Statistical Graphics. Alexandria, VA: American Statistical Association, pp. 4473‐4483. Bayer, J. and Bydžovská, B. and Géryk, J. and Obšívač, T. and Popelínský, L. (2012) Predicting drop‐out from social behaviour of students. In Kalina Yacef, Osmar Zaïane, Arnon Hershkovitz, Michael Yudelson and John Stamper. Proceedings of the 5th International Conference on Educational Data Mining – EDM 2012. Greece: www.educationaldatamining.org. pages 103–109, 7 p. Cook, D. and Swayne, D. F. (2007) Interactive and Dynamic Graphics for Data Analysis: With R and GGobi. Springer. Delavari, N. and Phon‐Amnuaisuk, S. and Beikzadeh, M. R. (2008). Data mining application in higher learning institutions. Fry, B. (2008) Visualizing Data, O'Reilly Media. Gilmore, W. (1989) The user‐computer interface in process control: A human factors engineering handbook. Elsevier Science. Han, J. and Kamber, M. and Pei, J. (2011) Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Elsevier Science. Hilpert, M. (2011) Dynamic visualizations of language change: Motion charts on the basis of bivariate and multivariate data from diachronic corpora, International Journal of Corpus Linguistics, John Benjamins Publishing Company, pp. 435‐ 461. Hochheiser, H. and Shneiderman, B. (2003) Dynamic querying for pattern identification in microarray and genomic data. IEEE International Conference on Multimedia and Expo. Jiang, J. and Wilson, Ch. and Wang, X. and Huang, P. and Sha, W. and Dai, Y. and Zhao, B. Y. (2010) Understanding latent interactions in online social networks. In Proceedings of the 10th annual conference on Internet measurement, IMC ’10, pages 369–382. ACM, New York, NY, USA. Jin, H. and Wu, T. and Liu, Z. and Yan, J. (2009) Application of visual data mining in higher‐education evaluation system. In Education Technology and Computer Science. ETCS ’09. Murray, S. (2013) Interactive Data Visualization for the Web. O'Reilly Media. Nosál, P. (2013) “Data mining in on‐line tests”, [online], Master Thesis, Faculty of Informatics Masaryk University, http://is.muni.cz/th/207773/fi_m/ Popelínský, L. and Briatková, M. and Kedaj, Z. (2008) DZEMUj: A Tool for Mining in e‐Learning Tests. Description and Experience. In Proceedings of the 7th European Conference on eLearning ECEL. Reading, England: Academic Conferences Limited. pages 299–303, 4 p. Robertson, G. and Fernandez, R. and Fisher, D. and Lee, B. and Stasko, J. (2008) Effectiveness of Animation in Trend Visualization. IEEE Transactions on Visualization and Computer Graphics. Piscataway, NJ, USA. Romero, C. and Ventura, S. (2007) Educational data mining: A survey from 1995 to 2005. Sacín, C. V. and Agapito, J. B. and Shafti, L. and Ortigosa, A. (2009) Recommendation in higher education using data mining techniques. In EDM, pages 191–199. Sirisack, S. and Grimvall, A. (2011) Visual Detection of Change Points and Trends Using Animated Bubble Charts. Environmental Monitoring, InTech. Theus, M. and Urbanek, S. (2011) Interactive Graphics for Data Analysis: Principles and Examples, Taylor & Francis. Ware, C. (2004) Information Visualization – Perception for Design. Morgan Kaufmann. Elsevier Science.

572

Strategies for Digital Inclusion ‐ Towards a Pedagogy for Embracing Student Diversity With Online Learning Baylie Hart Clarida, Milena Bobeva, Maggie Hutchings and Jacqui Taylor Bournemouth University, Bournemouth, UK bhartclarida@bournemouth.ac.uk mbobeva@bournemouth.ac.uk mhutchings@bournemouth.ac.uk jtaylor@bournemouth.ac.uk Abstract: This paper presents an early stage PhD research study that aims to develop a conceptual framework for effective learning approaches that influence digital inclusion and exclusion of diverse students. The study will move away from the traditional definitions of diversity and explore the different characteristics of diverse learners in a modern‐day setting using up‐to‐date technology. It will attempt to highlight factors that affect the experiences of online students and to offer practical guidelines for educators who are concerned with technology enhanced learning spaces in Higher Education (HE), and extends to adult education, training providers and further education. The findings could also impact on and benefit diverse learners by proposing strategies that facilitate individualised, needs tailored learning, particularly on blended learning programmes. A mixed methods study will evolve through two distinct phases: Phase 1 will draw on narratives using semi‐structured interviews and a full review of the literature, publications and journal articles. This will serve not only to explore the many characteristics of diverse students but also investigate current and emerging pedagogies in the field of educational research; Phase 2 will build on the findings from Phase 1. The findings will be restructured to generate a conceptual framework. A full evaluation will take place to test the reliability and validity of the framework with other diverse student groups and teachers by administering an online questionnaire survey. The focus of this paper is to outline the background, current literature, objectives and methodology of the research and to discuss the next steps. At the time of writing, a Pilot Study has been completed and the primary research is at the early stages of data collection. The Pilot Study resulted in a need to re‐word and re‐order the interview questions to gain richer data from the sample which is now being implemented in the interview process. It is anticipated that Phase 1 analysis will be completed by October 2013. Keywords: diverse students. technology enhanced learning (TEL). digital inclusion

1. Background Historically the digital divide and information communication technology (ICT) use (or non‐use) has been measured by a common set of demographics: gender, age, ethnicity, geography, socio‐economic status and educational background, (ONS, 2013). Yet the current trend for encouraging widening participation in higher education institutions (HEI) and the vast range of courses on offer in the United Kingdom (UK) has resulted in a much more diverse student population, compared to the traditional university population. It is fair to say that this basic set of measures, once used to determine student involvement with technology, could now be outdated so the time has come for a study to delve into the many characteristics of today’s HEI learner. Educational experience is also influenced by the Government. University costs have soared since the coalition government came to power, (UCAS, 2013). Coupled with this, the UK has experienced a recession (ONS, 2013) so financial savvy students have become picky consumers looking for a product that meets all of their needs. These days, students attend university for all sorts of reasons. Gone are the days when a university campus would mainly consist of college and sixth form leavers pursuing a four year taught degree to start a career. HEIs now cater as much for non‐traditional students undertaking short and top up courses, foundation degrees and professional development, as they do for once conventional students. Not only has the student population and courses on offer evolved but the way in which the courses are delivered has advanced too. A state‐of‐the‐ art online learning environment can offer blended learning and distance learning programmes that provide opportunities for a range of students to access course materials, collaborative software, discussion boards, wikis and other learning technologies at university, from home or on ubiquitous mobile devices (Holzinger et al., 2005). A blended learning model, (Bonk and Kim, 2005) combining face‐to‐face delivery with computer mediated activities, assists the learning process, (Means et al.,2009) and is becoming a popular choice for both student and educator. It is students choosing this mode of delivery that this research will examine. The research was motivated by the Business, Innovations and Skills (BIS) White Paper (2011) that recommends putting the student at the heart of education. It will extend understanding of diversity in technology enhanced

573

Baylie Hart Clarida et al. learning (TEL) to create more personalised and needs‐tailored pedagogic strategies and the resulting conceptual framework will be an original contribution to the body of knowledge in educational research. Specifically, the objectives of this research are:

To explore the characteristics and needs of diverse learners and develop a taxonomy of learners' needs.

To investigate current and emerging pedagogies for engaging students with collaborative technology enhanced learning and assess their value with a diversity of learners.

To incorporate the findings into a conceptual framework for effective learning approaches and of factors that influence digital inclusion and exclusion.

In meeting these research objectives, another question addressing the personalisation of the learning experience will be explored:

How do differences in learner characteristics impact on their experiences of using technology for learning?

2. Literature review Conventionally, diverse students are categorised by a widely used set of demographics. As far back as the 1800’s authors and researchers have talked about diversity in education. For example, Sir Edwards Taylor talks about “race”, “origin” and “culture” when he discusses writing and language learning in his book published in 1870, (Taylor, 1870 p.2). Yet much of the literature that focuses on student diversity was pre‐technological and certainly does not reflect the rapidly gaining momentum of advances in technology and its impact on the learner. “the past is continually needed to explain the future” (Taylor, 1870. p.2) In order to move forward to the future and to address the research objectives, a delve into the past is necessary. A thorough review of the current literature documenting diverse learners shows that only the common set of demographics is used. Little has been done to explore if the emerging type of HE learner, studying an up‐to‐date style program, can be measured using the same set of demographics or in fact if they have different characteristics. A study of university students conducted by Yorke and Longdon (2008) found that students failing to adjust to different and unfamiliar teaching and learning environments were ‘at risk’ of withdrawing from their program of study. Of those, mature students are more likely to ‘drop out’ in the first year of study compared to younger students (Coffield et al., 2004). According to Knowles et al. (2011), older learners, argued to be ‘digital immigrants’ by Prensky (2009), learn in a different way to their younger counterparts. Adult learners may need more academic, technical or pastoral support for self‐directed learning activities compared with younger students. However, despite these gloomy assertions over two thirds of students obtain qualifications later in life. A recent study shows that 71 per cent of people in England, Scotland and Wales achieved at least one qualification between the ages of 23 and 50, and more than half did so between the ages of 33 and 50, (The Institute of Education, 2013). The same study also showed a shift in genders obtaining qualifications. Women, between the ages of 33 and 50, were almost twice as likely as men to gain a qualification two or more levels previously held. In addition to qualifications, gender also impacts on how students learn, (Bennet and Marsh 2003, Wehrwein et al. 2006). Female students are less likely to speak out in a traditional face to face classroom environment yet in online course discussions are more likely to voice contributions, in turn impacting on deeper learning, (Anderson and Haddad, 2005). Lorenzo et al. (2006) argue that even digital natives, regardless of their gender, ethnicity or socio‐economic status, do not necessarily have exposure to or the skills needed to confidently use technology. Several universities in the UK encourage students to enrol on courses regardless of previous academic success but instead expect evidence of career experience in the subject area. This has resulted in mixed academic (proven) ability within cohorts (Wooden et al., 2001). A study into retention, attrition and support, by Haggis (2006) explores pedagogies for a range of diverse HE students. She proposes that with so many diverse students choosing HE, conventional support is unrealistic and that it is up to the educational establishments that provide for these students to move away from traditional support networks and concentrate on new teaching and learning approaches. Importantly, adapting courses so as to utilise new technology, will enable the diverse student population to access and learn the subject. Success on courses studied by multi‐cultural students, although more complex, is achievable when a variety of flexible support is offered and close monitoring of student progress is in place (McNaught and Vogel, 2004).

574

Baylie Hart Clarida et al. In addition, with the advancement of Web 2.0 technologies, and the anticipated Web 3.0 and Semantic Web, the effective, once classroom based, teaching method of student collaboration can still be achieved creating a virtual classroom in which to learn. Computer Mediated Communication (CMC) for collaborative discussion, whether synchronous or asynchronous, is commonplace in most universities offering online learning. Of course, in order to exchange information and construct knowledge (Salmon, 2004) asynchronous communication tools, such as blogs and discussion boards allow learners to go away and think about what has been said and their responses. The primary research will be synthesised with analysis of the key literature, (see Table 2).

3. Methodology A mixed methods study will be adopted. It will evolve through two distinct phases succeeding a preliminary Pilot Study: Phase 1 of the study will form the primary research and will attempt to address the first two research objectives. The primary data collection method will be interview questions and will be semi structured in such a way as to try and address both objectives, combined with a review of the literature, journal articles, publications and studies. This will serve to investigate current and emerging pedagogies and in addition, compare the findings from the interviews to current knowledge. Interviews will allow rich data to be generated (Westby et al. 2003), that other methods, such as observations and questionnaires cannot. If possible, the participants will be interviewed ‘in situ’ at their place of study to allow for their behaviours and experiences to be entwined with the social context of the study, (Heyle, 2001). The interviews will explore which characteristics of diverse students affect barriers to digital inclusion and uncover the wide‐ranging needs that influence engagement with blended learning programs. The primary data collection will allow for understanding of ‘why’ and ‘how’ the participant feels the way they do about the technology they are using in their studies. It will not only reduce any potential researcher bias but also encourage the participant’s voice to come through, (Denzin and Lincoln, 2000). The participant will be given the opportunity to tell me about themselves at the beginning of the interview, as they will be more likely to reveal the more important aspects of their demographic in this way, rather than asking a set of structured demographic questions first. The sample strategy for Phase 1 will start with a purposive sample, (Patton, 2002). Second year, undergraduate nursing students, with a range of diversities will be used for the primary research. This group was chosen for two reasons: firstly, there is convenient access to the group being part of the university within which the research will take place and secondly, it was thought that nursing students would include a diverse range of students. All of the participants will have completed the same course units so as to reduce any variables of past technology exposure during their current university experience. The unit is delivered using a blended learning approach. A sample of at least 20 participants is expected however, data collection will continue until saturation, (Moran, 2000). Where possible, further interviews will be conducted using a snowball strategy (Morgan, 2008) to enrich the sample. The interviews will be recorded using a digital Dictaphone, backed up by a voice recording PDA. They will be transcribed and coded to form categories. Qualitative Comparative Analysis (QCA), (Rihoux, 2006) is considered the most effective method to analyse the data as its systematic approach will examine relationships between the participant’s data and the potential outcomes. The QCA data generated from the study will be managed using data management software, in this case NVivo. The software will simplify organisation and cross‐examination of the data but will in no way conduct analysis or draw conclusions. A pilot study was conducted prior to Phase 1. It took the form of semi structured interviews with students from external universities on blended learning courses. This allowed the testing of planned interview techniques (Kvale, 1996), verification of the interview schedule and an opportunity to make any necessary changes to the questions. It also sought to test the equipment to be used. In this instance the equipment will consist of a digital Dictaphone which will allow the transfer of audio files to a computer for analysis and storage and a back‐up PDA with voice recording. Transcribing software, specifically Dragon Naturally Speaking, will be tested for efficiency and accuracy.

575

Baylie Hart Clarida et al. Phase 2 will build on the findings from Phase 1 and will seek to address research objective 3. The analysed patterns will be restructured in a logical manner to induce a framework and this will be compared with existing research to identify what should be included or excluded from the framework. A full evaluation will take place to test the reliability and validity of the framework with other diverse student groups and teachers from other universities in the UK and overseas. The method by which this will be achieved will be to conduct an online self‐administered questionnaire survey (Fowler, 2009).The advantage of this is that the participant can complete the survey in their own time, a wider audience can be reached and in little time. Participants will have time to formulate their answer and research shows that participants answer more honestly on online surveys than on paper surveys (Barribeau et al., 2012) and there is a higher response rate, (Parker, 1992). In addition, it will be more cost effective as no paper or telephone costs will be involved. Alongside that, websites that allow the researcher to create a survey have efficient storage and management systems which allow simple, preliminary organisation and comparison. The questionnaire survey questions will be a mixture of structured and unstructured questions and will also include Likert‐type Scaling, (Likert, 1932). The participants will be asked to evaluate certain statements, generated from Phase 1, based on their own experiences with TEL. Structured questioning will identify the full demographic of the participant and unstructured questioning will allow the participant to voice their views and comments about the questions being asked. A different sample strategy will be utilised for Phase 2 as this phase seeks to confirm or disconfirm the resulting framework developed from the findings from Phase 1. Therefore, using a snowball strategy, a different set of at least 50 students from external universities will be utilised. They will be on different courses and at different levels. In addition to the student sample, a sample of at least 40 teachers at HE institutions will also be surveyed to generate a different viewpoint. This process will be one of the ways in which the study will seek external validity. Other factors of internal and external validity will be considered throughout the process such as: researcher effects; research environment; sample size; variables and an audit trail. Other researchers have used a range of methods to evaluate frameworks and models, such as: focus groups, (Krueger and Casey, 2009); case studies, (Merriam, 1997); and experimental research, (Bailey, 2008). Miles and Huberman, (1994) suggest a range of methods to ‘test explanations’: ruling out false relationships; replication; examining rival explanations and receiving feedback from participants by member checking, which will also be implemented. Analysis of the survey responses will be similar to that in Phase 1. As the surveys will consist of open questions, the data will be categorised and analysed using the QCA approach.

4. Discussion The experience in implementing the research design has been influenced by the outcomes of the Pilot Study. A re‐wording and re‐ordering of some of the questions was required however, no data analysis took place to ensure no researcher influence during data analysis of the primary research. As the research seeks to determine the characteristics and needs of diverse learners, it is imperative that no anticipatory results are expected by the researcher or prompts given to the participant. Therefore, four initial questions will be asked in order to generate rich data about their characteristics and experiences. Neither researcher nor participant should be influenced by previous studies or research. Consequently, conventional demographic questions will be asked after the main interview has ceased and only if the participant agrees. Table 1 and Table 2 below illustrate the rationale for the interview questions. The initial interview questions are set out in the first column of Table 2. Table 1: Research objectives and question numbers Research Objectives and Question To explore the characteristics and needs of diverse learners and develop a taxonomy of learners' needs. To investigate current and emerging pedagogies for engaging students with collaborative technology enhanced learning and assess their value with a diversity of learners. To incorporate the findings into a conceptual framework for effective learning approaches and of factors that influence digital inclusion and exclusion. How do differences in learner characteristics impact on their experiences of using technology for learning?

576

Representing Number 1 2 3 4

Baylie Hart Clarida et al. Table 2; Research objectives and interview questions rationale Interview Questions and Variables

Supporting Theories

Tell me about yourself Can you tell me about your course How is technology used on your course? What are your experiences of using the technology on your course? Demographics Name Age Gender Any siblings Ethnicity Religion Marital status Any SEN/SpLD Any disabilities How would you describe your personality Commitments/external pressures Any children/ages/genders Where do you live Do you own your own home/rent/student accommodation Employment status/where/hours Interview Questions and Variables

Income Would you say your household income is low, medium or high When you were growing up, would you say your parents’ income was low, medium or high Motivation/Role models What were your parents’ professions Did your parents go to university Did/does any other family/friends go to university Is there someone that inspired you to go to university What is your motivation for doing the course What are your plans when you have finished the course Educational performance What was your highest level qualification when you left school What is your highest qualification now Your course On scale of 1‐10, how much are you enjoying your course and why Are there any expectations that you had before the course that haven’t been met What would make your course easier to complete

Research Objectives and Question 1 2 3 4

Older learners (Coffield et al. 2004, Knowles 2011) Digital immigrants (Prensky 2009) Digital natives (Lorenzo et al. 2006) Gender (Wehrwein et al. 2006) BME groups (Richardson 2008) Digital inclusion (Madden et al. 2009) SEN (Warnock 2010) SpLD (Anderson and Haddard 2012)

1 4

Emotions and e‐Learning (O’Regan 2003)

1 4

Supporting Theories

Socioeconomic status (Currie 2009) Parental effects (Dubow et al. 2009)

Research Objectives and Question 1 4

Ageism (Ford 2012) Motivation (Martin and Dowson 2009)

1 4

Previous educational experiences (Wooden et al. 2001)

1 4

Unfamiliar teaching and learning environments (Yorke and Longden 2008) Support (Haggis 2006) COP (Lave and Wenger 1998) Affinity spaces (Gee 2005) Blended learning (Bonk and Kim 2005, Means et al. 2010) Diverse student monitoring (McNaught and Vogel 2004) Constructing knowledge (Salmon 2004)

2 3

577

Baylie Hart Clarida et al. Interview Questions and Variables

Supporting Theories

What would make you enjoy the course more Which elements of the course do you like the least Which elements of the course do you like the most Technology What technological equipment do you use to access the online elements of your course Tell me about any problems you have had with the technology on your course How were any problems you had resolved Do you enjoy the technological elements of the course/why

Retention (Hughes 2007)

Support (Dodgson and Bolam 2002, Seidman, 2005) Support (Haggis 2006) CMC (Pilkington 2003) M‐Learning (Holzinger 2005) Blended learning (Means et al. 2010) Responses to technology (Taylor 2010) Diverse Students (McNaught and Vogel 2004) Constructing knowledge (Salmon 2004, Moule 2007) eLearning pedagogy (Britain and Liber 2004, Alexander, 2006) VLEs (Brown et al. 2006) eLearning design (Laurillard 2006) Wikis (Bower et al. 2006) Self‐directed study (Deepwell and Malik, 2008)

Research Objectives and Question

2 3 4

It also became evident from the Pilot Study that a post‐interview evaluation should be recorded. This serves two purposes: firstly it will help reflection and improvement of the interviewers’ technique and secondly, it will evidence reflexivity which will aid writing this section in the final thesis. This paper argues that there is a need to explore the many characteristics of a modern day learner utilising modern technologies to learn. Due to the inductive nature of the research there are no anticipated results or hypotheses, only that characteristics will emerge that have not been considered before, resulting in a more personalised learning experience for diverse students.

5. Conclusion There are many factors to consider with respect to TEL, which is now such a dominant feature of many HEI programs that it can hardly be ignored. Widening participation has encouraged a diversity of student, so it seems impossible to have a one‐size‐fits‐all model approach to TEL to accommodate a diverse student population. This research will explore more than the common demographics as outlined in the literature above. It will draw on student narratives of inclusion and exclusion and delve into the many characteristics that make up a modern student population.

References Alexander, B. 2006 Web 2.0 A New Wave of Innovation for Teaching and Learning? Educause March/April 33‐44 Anderson, D. and Haddad, C. 2005 Gender, Voice, and Learning in Online Course Environments. Journal of Asynchronous Learning Networks (JALN), Vol. 9:1 Bailey, R. 2008 Design of Comparative Experiments. Cambridge University Press Barribeau, P., Butler, B., Corney, J., Doney, M., Gault, J., Gordon, J., Fetzer, R., Klein, A., Ackerson Rogers, C., Stein, I., Steiner, C., Urschel, H., Waggoner, T. and Palmquist, M. (1994 ‐ 2012). Survey Research. Writing@CSU. Colorado State University. Available at http://writing.colostate.edu/guides/guide.cfm?guideid=68 accessed on 16.3.13 Bennett, S. and Marsh, D. 2003 Widening Participation and e‐Learning: Meeting the Challenge within a Foundation Degree, Journal of Widening Participation and Lifelong Learning, vol. 5, no. 3. BIS 2011 Students at the heart of the system at http://www.bis.gov.uk/assets/biscore/higher‐education/docs/h/11‐944‐ higher‐education‐students‐at‐heart‐of‐system.pdf accessed on 2.2.13 Bonk, C. and Kim K 2005. Future directions of blended learning in higher education and workplace learning settings. In C. J. Bonk and C. R. Graham (Ed), Handbook of blended learning: Global Perspectives, local designs. (pp. 550–568). San Francisco, Pfeiffer Publishing Bower, M., Woo, K., Roberts, M. & Watters, P. 2006 Wiki pedagogy ‐ A tale of two wikis. In Proceedings of the International Conference on Information Technology Based Higher Education and Training (ITHET 06), (pp.191‐202), Sydney, Australia Britain, S. and Liber. O. 2004 'A Framework for the Pedagogical Evaluation of eLearning Environment', JISC‐commissioned report

578

Baylie Hart Clarida et al. Browne, T., Jenkins, M., Walker, R. 2006 A Longitudinal Perspective Regarding the Use of VLEs by Higher Education Institutions in the United Kingdom. Interactive Learning Environments 14(2), 177‐192. Coffield, F., Moseley, D., Hall, E. and Ecclestone, K. 2004 Should we be using learning styles: what research has to say to practice. Learning and Skills Research Centre, London Currie, J. 2009. "Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development." Journal of Economic Literature, 47(1): 87‐122 Deepwell, F. and Malik, S. 2008 On campus, but out of class: an investigation into students’ experiences of learning technologies in their self‐directed study. ALT‐J Research in Learning Technology 16(1), 5‐14 Denzin, N. K., & Lincoln, Y. S. 2000 Handbook of qualitative research. Sage Publications, London Dodgson, R. and Bolam, H. 2002 Student retention, support and widening participation in the north east of England. Sunderland: Universities for the North East Dubow, E., Boxer, P. and Huesmann, L. 2009 Long‐term Effects of Parents’ Education on Children’s Educational and Occupational Success: Mediation by Family Interactions, Child Aggression, and Teenage Aspirations," Merrill‐Palmer Quarterly: Vol. 55: Iss. 3, Article 4 Ford, N. 2012 at http://www.helium.com/items/2360039‐unemployed‐older‐workers‐a‐problem‐in‐povery accessed on 22/11/12 Fowler, F. 2009 Survey Research Methods Vol. 1. Sage Publications, London Haggis, T. 2006 Pedagogies for diversity: retaining critical challenge amidst fears of ‘dumbing down’. Studies in Higher Education 31(5) 521‐535 Heyle, B. 2001 Ethnographic interviews. In P. Atkinson, A. Coffey, S. Delamont, J. Lofland, & L. Lofland (Eds), Handbook of ethnography (pp. 369‐383). London: Sage Holzinger, A., Nischelwitzer, A., and Meisenberger, M., 2005 Mobile phones as a challenge for m‐learning: Examples for mobile interactive learning objects (milos). Third IEEE International Conference on Pervasive Computing & Communications Workshops, 307‐311 Hughes, G and Lewis, L 2003 Who are successful online learners? Exploring the different learner identities produced in virtual learning environments, in: J. Cook and D. McConnell (Eds) Communities of Practice. Research Proceedings of the 10th Association for Learning Technology Conference (ALT‐C 2003) (The University of Sheffield and Sheffield Hallam University, UK Institute of Education at http://www.ioe.ac.uk/newsEvents/85353.html accessed on 9.7.13 Knowles, M., Holton, E. and Swanson, R. 2011 The Adult Learner 7th Ed. Elsevier Inc., Oxford Krueger, R. and Casey, M. 2009 Focus Groups: A Practical Guide for Applied Research 4th ed. Sage Publications Kvale, S. 1996 Interviews: An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage Laurillard, D. 2006 E‐Learning in Higher Education, in: P. Ashwin (Ed) Changing Higher Education: The Development of Learning and Teaching, London, RoutledgeFalmer Likert, R. 1932 A Technique for the Measurement of Attitudes. Archives of Psychology 140: 1–55Lorenzo, G., Oblinger, D. & Dziuban, C. 2006 How choice, co‐creation, and culture are changing what it means to be net savvy. EDUCAUSE Quarterly, 30(1). http://connect.educause.edu/Library/EDUCAUSE+Quarterly/HowChoiceCoCreationandCul/40008 accessed on 12.7.13 Madon, S. Reinhard, N. Roode, D. and Walsham, G. 2009 Digital inclusion projects in developing countries: processes of institutionalization. Information technology for development, 15 (2). pp. 95‐107. ISSN 0268‐1102 Martin, A. and Dowson, M. 2009 Interpersonal Relationships, Motivation, Engagement, and Achievement: Yields. Review of Educational Research. 79, 1; Research Library pg. 327 McNaught, C and Vogel,D. 2004 The converging streams of globalisation and eLearning. Stretching the comfort zone. In R. Atkinson, C. McBeath, D. Jonas‐Dwyer and R. Phillips (Eds), Beyond the comfort zone:Proceedings of the 21st ASCILITE Conference (pp. 637‐646). Perth, 5‐8 December Means, B., Toyama, R., Murphy, Y. Bakia, M. and Jones, K. 2009 Evaluation of evidence‐based practices in online learning: A meta‐analysis and review of online learning studies. Washington, D. C: U.S. Department of Education Merriam, S. 1997 Qualitative Research and Case Study Applications in Education. Jossey‐Bass Publishers, CA Miles, M. and Huberman, A. 1994 Qualitative Data Analysis, 2nd Ed., p. 10‐12. Newbury Park, CA: Sage Morgan, D. 2000 Introduction to Phenomenology. London and New York: Routledge Morgan, D. 2008 The SAGE Encyclopedia of Qualitative Research Methods. SAGE Publications, Inc. Moule, P., 2007. Challenging the five‐stage model for e‐learning: a new approach. ALT‐J, 15(1), pp.37‐50 Office for National Statistics (ONS) 2013 at http://www.ons.gov.uk/ons/index.html accessed on 10.5.13 O’Regan, K. 2003 Emotions and e‐learning. JALN 7 (3) 78‐92 Parker, L. 1992 Collecting data the e‐mail way. Training and Development, 52‐54 Patton, M . 2002 Two Decades of Developments in Qualitative Inquiry: A Personal, Experiential Perspective Qualitative Social Work September 2002 1: 261‐283 Pilkington, R. 2003 Reflecting on roles: Using synchronous CMC to develop a knowledge‐ building community amongst post‐graduates. International Journal of Continuing Engineering Education and Life‐Long Learning (special issue on technological support for new educational perspectives), 13(3/4), 318‐335 Prensky, M., 2009 H. Sapiens digital: From digital immigrants and digital natives to digital wisdom. Innovate: Journal of Online Education, 5 (3)

579

Baylie Hart Clarida et al. Richardson, J. 2008 The attainment of ethnic minority students in UK higher education. Studies in Higher Education. 33 (1), 33‐48 Rihoux, B. 2006 Qualitative Comparative Analysis (QCA) and Related Systematic Comparative Methods: Recent Advances and Remaining Challenges for Social Science Research International Sociology September 2006 21: 679‐706 Salmon, G. 2004 E‐moderating‐The Key to Teaching and Learning Online, Second edition, RoutledgeFalmer, UK Seidman, A. 2005 Reading Improvement: Project Innovation, Alabama. Vol. 42:1 Taylor, E. 1870 Researches into Early History of Mankind and the Development of Civilization. Bradbury, Evans and Co., London. Tayor, J. 2010 Psychological and contextual issues in technology‐enhanced learning: individual differences and the student emotional experience. Pages 37 ‐ 40. UCAS. 2013 Student Finance. Available: http://www.ucas.ac.uk/students/studentfinance/ Accessed 9th March 2013 Warnock, M 2010 Special educational needs: a new look, In: M. Warnock & B. Norwich, Special educational needs: a new look, ed. L. Terzi. (London, Continuum) Wehrwein, E., Lujan, H and DiCarlo, S. 2006 Gender differences in learning style preferences among undergraduate physiology students doi: 10.1152/advan.00060.2006 Adv Physiol Educ 2007 vol. 31 no. 2 153‐157 Westby, C. , Burda, A. and Mehta, Z. 2003 Asking the Right Questions in the Right Ways : Strategies for Ethnographic Interviewing. The ASHA Leader Wooden, M. VandenHeuvel, A., Cully, M. and Curtain, R. 2001 Barriers to training for older workers and possible policy solutions. Department for Education and Training and Youth Affairs, University of South Australia Yorke, M. and Longden, B. 2008 The First Year Experience of Higher Education in the UK. York: Higher Education Academy

580

GeoGebra in Teaching Linear Algebra Veronika Havelková Charles University in Prague, Faculty of Education, Prague, Czech Republic veronika.havelkova@pedf.cuni.cz Abstract: This contribution will present a study that focuses on the potential of using the GeoGebra software in a linear algebra course for pre‐service teachers. This particular freeware enhances dynamic geometrical representations of both simple and more complex algebraic operations. Consequently, it seems suitable for the teaching and learning of linear algebra. The experiment was conducted during a practice session that complemented lectures in linear algebra. This semester‐long course is compulsory to second‐year students in the undergraduate program for future teachers of mathematics. The primary aim of implementing GeoGebra in the course was to offer students further representation of algebraic operations, supporting their abstract thinking. The secondary aim was to introduce the future secondary school teachers to the didactical advantages of the software. The main research question inquired whether using a set of pre‐ designed applets would lead to a better understanding of selected algebraic operations. The answers were based on two kinds of evidence: the objective outcomes that students produced in solving selected problems and the subjective response to this extra support (i.e. whether the students see the new representation as helpful). The experiment involved two different cohorts; the group consisted of a total of 34 students, who had attended various lectures on linear algebra prior to the practical seminars. During the seminar session they saw geometrical representations in dynamic applets designed in GeoGebra. The influence of this tool was studied with instruments of both qualitative and quantitative analysis. The method followed a quasi‐experimental model of pre‐ and post‐tests: a questionnaire was used at the beginning of the experiment to determine whether students could represent linear functions, systems of linear equations (in terms of the number of solutions) geometrically. When testing the ability to geometrically represent matrices, adding and multiplying matrices and determinants, students were asked to geometrically represent simple examples. Operational thinking necessary for matrix addition was prompted by an unfinished sentence. The students indicated on a numerical Likert‐like scale what importance they gave to geometrical representation and dynamic geometry in helping them to understand algebraic topics. A similar questionnaire was used as a post‐test at the end of the session. The questionnaire had been piloted on a small group of students and then adapted accordingly. The data was analyzed using relevant statistical methods. Although the number of students does not allow me to generalize the results, the group found the use of the program successful. Not only did the software help them deepen their knowledge but the students appreciated the didactic potential of this tool along with its versatility. In class the students freely expressed their enthusiasm for the geometrical representations: that they liked them and that they enhanced their understanding of the concepts in further complexities. The statistical results of the test confirmed this notion. A paired two‐tailed t‐test (α= 0.01) also showed a positive change in the students’ expectations of the use of geometrical representations in class. These findings further inspired me to implement the relevant applets into an e‐learning course. Keywords: linear algebra, GeoGebra, dynamic mathematics

1. Introduction New technologies have the potential to support education across a curriculum and provide opportunities for effective communication between teachers and students in ways that were not possible before (Lustigová, 2012). The use of ICT in class has a number of positive aspects: it encourages individuals to investigate on their own and to change qualitatively the content and flow of cognitive processes involved in their problem‐solving (Pea, 1985). ICT brings many advantages to a mathematics class: it fosters concentration during more complex problem‐solving that requires both lower‐ and higher‐level thinking, the former including the execution of individual steps in the process, the latter consisting in choosing a solving strategy – i.e. all one has to do to arrive at the solution (Kutzler, 2000). Frequent alternation between these two levels demands a high level of concentration and can cause errors easily. Thus, a suitable program can help a problem‐solver with the lower‐level tasks (e.g. elementary calculation) so that they can focus on the solving process as a whole. In addition to this, including visualization in the presentation of a topic decreases the required current memory capacity ‐‐ which is, for an individual, limited (Resnick, Johnson, 1988). The freed mental capacity can be used for intensive thinking. During a non‐visual presentation, we typically use most of our current memory capacity to visualize the situation presented/explained. One of the important advantages is that students enjoy working with ICT tools and feel motivated for the subject; teachers perceive their teaching as more effective (Vondrová, Jančařík, 2012). Currently, mathematics teaching has been enhanced by the environment of dynamic geometry software. This environment belongs among the types of cognitive technology with the highest level of didactical application.

581

Veronika Havelková It is also most used and sought‐after by mathematics teachers (Vaníček, 2009). This environment is gradually being replaced by the environment of dynamic mathematics software which features dynamic links between algebra, geometry and calculus through applets. By the term “applet” we will understand a set of displayed objects. An applet allows for dynamic display of (among others) geometrical situations. Unlike a moving image, an applet can be managed by its user who can change some of the objects in it while the pre‐designed invariants remain the same). These three areas form a fundamental part of teacher preparation programs (Adamec at al, 2009). Making connections between mathematical concepts so that they form a consistent whole is definitely one of the higher goals of mathematics teaching. The tools chosen to this end could be geometrical representations of simple or more complex algebraic operations, etc. At a university level, mathematics is perceived as more difficult because students find it more demanding in terms of abstract thinking. An additional representation of concepts covered in university mathematics courses thus not only offers an advantage but should form an indispensable part of the course work. I chose the subject of Linear Algebra as an area of investigation. This subject generally includes several modes of description of the basic objects and operations of linear algebra. These modes include the abstract mode, the algebraic mode and the geometric mode (Hillel, 2000). Anna Sierpinska (Sierpinska, 2000) provides a number of cases that illustrate students´ reluctance to enter into the structural mode of thinking, and, first and foremost, their inability to move flexibly between the three modes. Students do not deal with matrix representations of these systems, questions about existence and uniqueness of solutions, the link to analytic geometry of lines, plane and space, geometric transformations, matrix algebra and determinants, etc. (Harel, 2000). The Linear Algebra course at the Faculty of Education at our university is part of the core coursework, usually offered in the second year of the mathematics education programme. This course has been developed and taught by the responsible professor‐educator for many years, and is been based on on the theory of didactic situations, as presented in Brousseau (1997). That is why the outline of the seminar session in this investigation was inspired by the same theory. Creating the specific applets used in the session was guided by the concepts of the theory of generic models (Hejný, 2004). The individual applets were thus designed to provide separated models (Hejný, 2004) that would lead to the abstract lift (Hejný, 2004) towards the desired generic model (Hejný, 2004). The primary aim was to provide students with an additional geometric representation which could enhance their understanding and help them move flexibly between the three types of representations. The secondary aim was to present the didactic potential of the program GeoGebra to our students ‐ future mathematics teachers in secondary schools. The guiding research question was the following: Will the use of prepared applets enhance students’ understanding of selected algebraic operations?

2. Material and methods This research was conducted on two groups of students attending (separately) one 90‐minute seminar (practice) session in Linear Algebra (the total of students was 34 – 24 female and 10 male students). These sessions were built around interactive opportunities to get insight into the geometrical representation, with the use of dynamic applets created in GeoGebra (all the applets were original and now are made available at http://www.geogebratube.org/user/profile/id/716). Students had attended a series of lectures that covered the concepts of matrices and determinants. Many of the students had had some experience with college courses, or were repeating this particular course. There were 18 students who were taking college courses for the first time. Out of the 16 remaining students more than a half had taken college math courses. I studied the impact of the practice session in question with a combination of qualitative and quantitative tools. The research was conducted as a quasi‐experiment of the pre‐test/post‐test model. The shortness of the period between both tests and the fact that both the session and the test focused on geometrical representations could have affected the results of this particular study. At the beginning of the session, a questionnaire was given to students to gauge the students’ ability to represent systems of linear equations (in terms of the number of solutions) geometrically. The questions were answered orally. For measuring the ability to geometrically represent matrices, matrix addition, matrix multiplication and determinants, students were given several simple examples to draw in any way they could. Their operational thinking concerning the addition of matrices was examined with an open‐ended prompt: “When we add matrices, we proceed like this: ...” As expected, a partial result of the study was finding out what relevance students give to seeing geometrical representations in terms of understanding the underlying

582

Veronika Havelková algebraic concepts, and to what degree they believe that the presented applets were helpful (here they evaluated on Likert scale with values 1 to 5, where 1 was marked as “did not help at all” and 5 as “was very helpful”). The post‐test was similarly conceived, and administered to students at the end of the session. The final form of these tests was based on a pilot version which was tested on four students who eliminated potentially ambiguous questions. An independent interview with each of these students was conducted after administering both pilot questionnaires. The pilot tests helped to ensure clarity of the final versions of the tests used in the study. The data from pre‐test and post‐test was entered in an MO Excel 2010 table, and the answers were further analyzed with tools of quantitative analysis.

3. The experiment I was using a series of prepared applets and leading a discussion about them throughout the session. Importantly, the constant manipulation with the applets enabled students to see an immense number different solutions (which, in turn, enabled them to start thinking parametrically, something that we could not achieve with a static picture). The search for answers to various questions was always done with the whole group of students and when possible, was accompanied by manipulating the applet. We started off by determining the number of solutions in a system of linear equations; a matrix representation of a system of linear equations was part of the applet (Fig 1). Moving the yellow points resulted in a change of the system and its solution (marked as a red point as well as an ordered pair of coordinates. The exploration included cases of parallel, intersecting and coincident lines. This applet is simple and can be used (without the matrix) in lower secondary maths classes. In this case, it was used to prepare students for the examples of geometrical representations that would follow. I had chosen to start with a commonly understood example that has been internally grasped and accepted in order to motivate the participants.

Figure 1: A system of linear equations The initial applet was followed by work with vectors and matrices: applets were used to show students the multiplication of a matrix by a scalar. The matrix was represented as an ordered pair of vectors where each column represents a vector depicted graphically in the applet. We changed the original matrix (as well as the solution matrix) by manipulating the vectors and by varying the value of the scalar. Using colour coding was important here; the different colours for vectors and matrices helped to see and understand the underlying concepts. The next applet showed matrix addition. Since the students had learned the representation of a matrix as an ordered pair of vectors from the previous applet, now we focused on further issues, looking for answers with the applet’s assistance: If we can interpret a matrix through vectors, how could we graphically solve a matrix addition problem? Finding the answer to this question proved rather easy: students had learned graphical addition of vectors in secondary school, in classes of both mathematics and physics. The questions that followed were in consistence with the content covered in lectures (i.e. questions that students could have answered without the graphical representation), for example: Is it possible to add two matrices of different types? Is matrix addition commutative? Will the swap of two columns in a matrix affect the result of the addition? A similar procedure was enacted for the numerical and geometrical subtraction of matrices.

583

Veronika Havelková The above described applets were accepted by the students without apparent problems, surprise or excitement. I can explain this phenomenon by considering that these applets had so far represented no added value to the students. They could have been a novelty but the addition and subtraction of matrices in their algebraic form presented no problems to the students at this point anyway, and their respective algorithms were perceived as completely natural. A different phenomenon, however, could be observed when it came to matrix multiplication and the series of applets used to represent it. Many students indicated lack of confidence when asked “How do we multiply matrices?”. They were generally aware of the algorithm, which they tried to describe with hand and arm gestures. Although they remembered it well, they had not taken ownership of it, as they clearly did not grasp the meaning of the steps or the procedure itself. In other words, the understanding of the procedure was mostly formal. I believe that it was for this reason that the applet representing matrix multiplication (Fig 2) brought a surprise element to the session. The first matrix was represented as an ordered pair of vectors, determining the new base, and the second matrix as a vector in this new matrix. While the nature of the multiplication algorithm had remained hidden to the students up to that point, the graphical representation offered a fairly simple and comprehensible method of solution, with the opportunity to see the underlying meaning of the calculation algorithm. The next question arose naturally: How will the situation change if the second matrix is of the type 2x2? The applet demonstrating this situation was more complex. However, students did not encounter big difficulties in understanding it: the abstraction needed was easier to perform for these students because they had observed the previous applet.

Figure 2: Matrix multiplication The next surprise came with the applet that demonstrated the meaning of a determinant of a matrix (Fig 3). Students’ understanding of its definition was purely formal and they could not recall its exact form (what they remembered was that it was “something to do with permutations”, or the algorithm for its calculation). The simplicity of the idea of graphical representation of determinants as areas (or volumes, etc.) was a great surprise for them. Observing this applet sparked genuine excitement in the class. Having worked with matrices of the type 2x2, we also mentioned the solution for a matrix of higher dimensions. To demonstrate a situation where we look for a determinant of a 3x3 matrix, I had prepared an applet which simulated the case in 3D. We discussed and justified several theorems connected with this topic, e.g. the theorem about the determinant of a triangular (square) matrix, or about the product of two determinants. The above mentioned applets achieved the given instructional aims of the Linear Algebra course. I used the rest of the session to present applets that dealt with the concepts of a course which the students would need to take later on – the Analytical Geometry II course – and which involves the study of transformations in analytical geometry. The role of these applets, therefore, was motivational and, at the same time, students were given the opportunity to observe how deeply interconnected these two areas of mathematics are. I returned to the concept of matrix multiplication by showing students an applet that represented it as a kind of transformation (Fig 4) and consequently discussing this new way of applying the original concept with them.

584

Veronika Havelková From here the students quickly discovered that matrix multiplication can be linked with concepts of secondary school mathematics, i.e. translation, rotation or reflection. Although this was not the primary aim of our session, some students asked to be shown several applets from the area of geometrical transformations (Fig 5).

Figure 3: Determinant value These were the main elements that assured that the session would attain its goals: the collaborative aspect of the session, the effective manipulation with objects in individual applets, and the visually demonstrative power of the prepared applets (i.e. the use of visual effects: colours, line strength, the form of embedded dynamic text etc.).

Figure 4: Matrix multiplication as transformation

Figure 5: Reflection as matrix multiplication

585

Veronika Havelková

4. Results and discussion 4.1 Question 1 The first question: “What does a linear function represent geometrically?” was meant to lead students towards thinking geometrically about algebraic concepts. This question was elementary on purpose: it could motivate students because they would know the answer. All students answered “a line”, except for one student who wrote “It is constant, it is a line segment”. In the post‐test, all stated “a line” in the answer.

4.2 Question 2 Question 2: “What is the geometrical representation of a situation where a system of linear equations has: a unique solution (2a); infinitely many solutions (2b); no solution (2c)?” was apparently more challenging, although the answer could be produced easily by pupils in the last year of lower secondary school. Accepted answers included: a point, an intersection (2a); coincident parallel lines (2b); non‐coincident parallel lines (2c). No answers in the pre‐test considered the possibility of a system of more than two linear equations. There were also some completely wrong answers. Some students used the term curve in question (2a), but no answer included the concept of intersecting curves or sets of points. Students were generally more successful in the post‐test. Eight students gave answers that considered systems of more than two linear equations. Table 1 gives more detail regarding the results in both the pre‐ and post‐test. Table 1: Results in both the pre‐ and post‐test

Pre‐test

Post‐test

Question

Correct

Incorrect

The null hypothesis, i.e. that there will be no significant difference between answers to the same question in pre‐ a post‐tests, was testes by a paired t‐test. The test criterion was higher than the critical value (33)=2,035: question 2a yielded the test statistic value of 2.098, question 2b yielded the test statistic value 2.659, question 2c yielded the test statistic value 2.659. Therefore, the alternative hypothesis, i.e. students’ results differed in both tests (for these three questions) was accepted.

4.3 Question 3 Question 3 was in the form of this prompt: “When we add matrices, we proceed like this: ...” All students (except for one blank answer) answered this question in the following general manner: “We add the values of corresponding coordinates”. Some replies included a drawing of the situation, either a particular case or a general one. The exact definition was not provided by any student. Thirty‐one students answered in the same fashion in the post‐test, and five students included the geometrical representation based on vector addition (two students included both representations). It is possible that this is the result of the fact that the algebraic solution in this case is clear and students accept it without difficulties. The geometrical representation here is understood rather as a point of interest.

4.4 Question 4 Question 4: „Try to explain to someone, in the simplest way possible, what determinant is (imagine you want to explain the meaning to someone who does not know anything about linear algebra).” yielded big differences between both tests: fifteen students left a blank in the pre‐test, sixteen students used words but provided no geometrical representation (the exact wording can be seen in Table 2), three of the students did use the geometrical representation. It needs to be noted that two of the latter had been studying at the university in the previous two years (one of them originally took part in an undergraduate program at the Faculty of Physics and Mathematics, the other student did not provide information about his previous university experience). In the post‐test, the situation changed completely: 21 students explained determinants using the geometrical representations of area and volume, 12 students attempted to generalize this concept, (as “super‐volume”). In the case of this question, the geometrical representation proved to be accepted more willingly than the

586

Veronika Havelková algebraic one. I attribute this to the contrast between the complexity of the algebraic definition (which, even if memorized, escapes the students’ understanding) on one hand, and the simplicity of the geometrical representation on the other. Table 2: The exact wording is denoted like a square matrix it is a system of numbers, the same number of columns and rows element over T determinant is a "substitute" for a system of linear equations It is a specific number which characterizes each matrix and which enables us to work out further characteristics. We can determine this number by a special algorithm applied to each matrix. It is denoted as the absolute value of a matrix, but unlike a matrix, it has a value. the sum of all the products of pairs of the elements across all permutations the number that we get if we add up all the permutations of its elements a square matrix of the n‐th order I will transform a system of equations where the number of equations equals the number of unknowns into such equations that each line contains one less unknown → I then multiply the numbers on the diagonal. an element of a solid for which the following is true (in a square matrix): Σπ=1…n zn π a1i1. a2i2… anin Determinant is a number which is the result of such equation transformations when we mult. a row (‐2), I have ‐1/2 in front of the matrix (i.e. divide by ‐2). It is the calculation of a given matrix. Determinant is a characteristic of a matrix. A number that we must calculate using the Laplace rule. Other than that I don’t know. I would like to know myself :).

4.5 Question 5 Question 5 contained three problems from linear algebra. The problems focused on matrix addition (5a), matrix multiplication (5b), and the value of a determinant of a matrix (5c). Here, too, a difference was evident (see Table 3) between the two tests. In the pre‐test, students often skipped the questions, crossed them out or wrote that it was not possible to solve them in this way. Question 5a yielded five correct solutions (three of these students were in their first year of studying mathematics at the tertiary level; two students were in their third year of the program). The other two problems had even worse results. This situation changed dramatically in the post‐test, as can be seen from the table. Many students solved the problems correctly. They were the least successful in solving problem 5b. It is necessary to note that this problem was a bit more challenging than the other two and fifteen of the students that fall into the category “evidence of attempted axis‐based solution”, marked their axes wrongly (i.e. they did not work with the vectors as with ordered pairs of vectors), which led them to an incorrect solution, even though the remaining steps were carried out correctly. You can see the correct and incorrect solutions of the problem 5b in Fig 6 and Fig 7. Table 3: Difference between the two tests

Pre‐test

Post‐test

Question

Blank

Attempt at axis‐based solution

Correct

587

Veronika Havelková

Figure 6: Correct solution

Figure 7: Incorrect solution

4.6 Question 6 Question 6 was used to determine what expectations students had of geometrical representations. The pre‐ test version “To what extent do you think would geometrical representations help you understand the linear algebra content?” was slightly altered in the post‐test: “To what extent do you think can seeing the geometrical representations help you understand the linear algebra content?” Students gave their answers on Likert‐type scale with values 1 to 5, where 1 was marked as “did not help at all” and 5 as “was very helpful”. The null hypothesis, i.e. that there will be no significant difference between answers to this question in pre‐ a post‐tests, was testes by a paired t‐test, on a 1% level of significance. The test criterion was higher than the critical value (32) = 2.738: the test statistic value was 3.288. Therefore, the alternative hypothesis, i.e. students’ expectations changed after the session, was accepted. A closer look at the mean value will show that the expectations rose from mean value 3.88 (with variance 0.80) to 4.33 (with variance 0.73). This suggests that students had expected a positive effect of geometrical representation on their understanding and the session convinced them even further. Graph 1 (Figure 8) gives a better idea of the frequency distribution in pre‐ and post‐tests.

Figure 8: Graph 1

4.7 Question 7 Question 7 was designed to estimate what expectations students had of the dynamic geometry software. In the pre‐test the question described the idea of such software: “To what extent do you think would geometrical representations available through dynamic geometry software help you understand the linear algebra content? Dynamic geometry software is a computer programme in which you can draw geometrical objects and then further drag them across the screen, change their length and size, or move them. You can change the individual objects when needed. These programmes are for example Cabri, and GeoGebra.” The post‐test version was shortened to: “To what extent do you think did the geometrical representations in GeoGebra help you understand the linear algebra content today?” Students gave their answers on Likert‐type scale with values 1 to 5, where 1 was marked as “did not help at all” and 5 as “was very helpful”. The null hypothesis, i.e. that there will be no significant difference between answers to this question in pre‐ a post‐tests, was testes by a paired t‐test, on a 1% level of significance. The test criterion was higher than the critical value (32) = 2.738: the test statistic value was 3.413. Therefore, the alternative hypothesis, i.e. students’ expectations about the

588

Veronika Havelková use of dynamic geometry software changed after the session, was accepted. Again, we can get a more detailed picture of frequency distribution in both tests from Graph 2 (Figure 9).

Figure 9: Graph 2

5. Conclusion The results of this study cannot be generalized. Nonetheless, the use of the GeoGebra software as a support tool for teaching linear algebra met with success in this particular experimental group of students. Not only did it help deepen their knowledge, it also demonstrated the didactic potential and versatility of this programme. Students expressed their enthusiasm for the geometrical representations already during the lesson; they indicated that these were helpful in getting insight into the given concepts. This initial impression was confirmed by the results of the post‐test. I also received feedback from the course lecturer who stated that students valued the session and its contribution to their learning. Furthermore, some students remembered and discussed this particular experience during one of the didactical maths courses (Havelková, 2011). Even though I value both aims of the study equally, I feel that the second one was more important (for future teachers of mathematics) because that aim is often ignored. On that account, I am very grateful to have been given the opportunity to pursue both aims at the same time. Combining them is, in my opinion, the best practice. These findings also inspired my implementation of the given applets into an e‐learning course.

Acknowledgements This paper was written with the support of Project SVV 267‐402: Quality in teaching and education.

References Adamec, M., Jančařík, A., Jančaříková, K. et al. (2009) “ICT in the Education of Future Teachers“, Aplimat 2009: 8th International Conference, Proceedings, Bratislava, pp. 637‐643. Brousseau G. (1997) Theory of Didactical Situation in mathematics, Dordrecht: Kluwer Acad. Publ. Harel, G. (2000) “Three principles of learning and teaching mathematics “,On the Teaching of Linear Algebra, Dordrecht: Kluwer Academic Publishers, pp. 177–189. Havelková, V. (2011) “Podpora výuky funkcí s programem GeoGebra“, Dva dny s didaktikou matematiky 2011, Prague, pp. 33‐34. Hejný, M. (2004) “Mechanizmus poznávacího procesu“, Dvacet pět kapitol z didaktiky matematiky, Prague: Faculty of Education, Charles University in Prague, pp. 23‐42. Hillel, J. (2000) “Modes of description and the problem of representation in linear algebra “, On the Teaching of Linear Algebra. Dordrecht: Kluwer Academic Publishers, pp. 191–207. Kutzler, B. (2000) “CAS as pedagogical tools for teaching and learning mathematics“, Proceedings of 2nd Mediterranean Conference on Mathematics Education, Nicosia, pp. 142‐160. Lustigová, L. (2012) “ICT Challanges in the 21st Century Business English University Classroom“, Journal on Efficiency and Responsibility in Education and Science (ERIE 2012), vol. 5, no. 2, pp. 46‐62. Pea, R. D. (1985) Beyond amplification: Using the computer to reorganize mental function, Psychologist, vol. 20, no. 4, pp. 167‐182. Resnick, L. B. and Johnson A. (1988) Intelligent machines for intelligent people: cognitive theory and the future of computer‐ aided learning, Pittsburgh: University of Pittsburgh Press. Sierpinska, A. (2000) “On some aspects of students’ thinking in linear algebra‘‘, On the Teaching of Linear Algebra. Dordrecht: Kluwer Academic Publishers, pp. 209–246. Vaníček, J. (2009) Počítačové kognitivní technologie ve výuce geometrie, Prague: Faculty of Education, Charles University in Prague. Vondrová, N., Jančařík. A. (2012) “Implementation of Netbooks in the Teaching of Mathematics in the Primary Schools“,11th European Conference on e‐Learning (ECEL 2012), The Netherlands, Univ. of Groningen, pp. 567‐574.

589

E‐Learning Based Preparation for Educational Activities Outside of School Jiří Hoffman University of Ostrava, Ostrava, Czech Republic hoffmannjiri@gmail.com Abstract: The presented work is based on behavior observation of different groups of students moving around in a non‐ school educational institution, in this instance, a technological museum. The work deals with a methodology proposal created in order to prepare elementary school students for visiting such an educational institution. Information that students need to acquire before visiting the museum is available on the school web pages, which are based on Instructional System Design principles. These principles are used for development of e‐learning courses. Such web pages offer various levels of depth of information consistent with the depth of interest of individual students. The article presents a concrete task called “Simple Machines”. These machines are interactive mechanical 3D exhibits with which the students will become acquainted during the visit to the museum. The purpose of the article is to test whether enhancing classroom education with the possibility of physically handling these aids will contribute to a better and lasting learning experience rather than studying only through a computer based multimedia course. The verification of this assumption is done by pedagogical experiment while visiting technological museum. This experiment verifies the growth and sustainability of students' knowledge. The students' responses to the preparation for visiting the exhibition are entered in a questionnaire. Keywords: interactive mechanical 3D exhibit, technological museum, simple machines, e‐learning course, pedagogical experiment, museum education

1. Introduction The primary role of a museum is to introduce the visitor to an interesting exhibit. A museum can also be used for educational purposes. Many types of museums (technological museums, natural science museums, etc.) allow a preparation of unique and illustrative lessons. Physical handling of the authentic exhibit in an atypical museum environment is certainly more motivating than regular classroom education. Using exhibits allows for the creation of a completely different way of educating, as is, for example, research oriented activity. The process of acquiring information is carried out more naturally since students are more engaged in the lesson. This way of learning can be considered a form of a playing, rather than learning (as conventionally defined). Therefore, such way of learning is called learning by physical experience and experiential education.

2. Theoretical background 2.1 Using a museum as an educational institution The American Alliance of Museums Education Professional Network (EdCom) sees the museum's mission as a place of lifelong education supporting practical education for various groups of people (edcom.org, 2011). Education in museums encompasses exploration, critical thinking, research, observation and discussion. The goal is to acquire new attitudes, skills, competencies and so on. The mission of the Museum Education field is to strengthen the role of museums. A museum is regarded as a public institution for educational purposes and as such integrates instructional and educational activities into the exhibition space. It can hold educational activities in line with the presented exhibits or cooperate with other school or non‐school institutions. Museum education is concerned with the purposeful, facilitated and intentional influence of a museum on the public (Šobáňová, 2012), through the means of specialized exhibitions (e.g., children's museums, museums for teenagers), workshops and specialized tours. To achieve that, museum education uses verbal, demonstrative and practical methods. According to Průcha (Průcha, 2000), a museum can be perceived as an educational institution, because during the exhibition tour, one subject (the guide) teaches while another (the visitor) is being taught. The educational potential of a museum can be enhanced by other activities beyond the standard presented exhibit. However, those activities must have specific educational objectives and prearranged content (Hooper‐Greenhill, 1991).

590

Jiří Hoffman

2.2 Museum education as a process While preparing students for visiting a non‐school educational institution, it is needed to think of museum education as a process. This process can be pictured through Herbat's didactic triangle, which was adjusted for museum education by Šobáňová (Hooper‐Greenhill, 1991).

Figure 1: Herbat's didactic triangle adjusted for museum education (Šobáňová, 2012) Šobáňová stresses the interconnection of individual relations between the components of the process with the goal of maximizing understanding of the context. The process of museum education can be further divided into two types of learning:

Indirect learning – interaction of exhibits and didactic elements placed in the exhibition with the visitor. Visitor acquires information based only on the depth of his interest. If the visitor does not wish to become part of the prearranged educational process, he can remain only a spectator. There are various educational activities based on the type of a museum.

Direct learning – prearranged and organized transfer of comprehensible educational content in the presence of a teacher or educators. This form of learning is both more beneficial and motivating for the visitor. A teacher's presence, guidance and motivation has an influence on the rate of students' information acceptance (Hooper‐Greenhill, 1991).

2.3 Educational strategies of a museum While preparing managed education in a museum, it is necessary to specify educational activities. Ideally the museum has its own educational strategies. Such strategies consist of educational objectives and a mission statement. When a museum already has such strategies, they can be used by a teacher while preparing. However, the museum is not bound to adhere to any curricula. In case of missing educational strategies, the teacher has to prepare such strategy by himself (LondonMuseums.org, 2011).

2.4 Teacher's preparation A teacher who wishes to conduct a lesson in a museum has to be well acquainted with the particular section of his field concerning the lesson. He also needs to know the current curricula adopted by his school, as well as the content of the museum's exhibition and the various possibilities of using the exhibits in the lesson. It goes without saying that the teacher is expected (and should be allowed) to visit the exhibition ahead of time. Based on available information, the teacher creates his educational strategy and suitable teaching methods. Shulman defined the process of acquiring knowledge by the term pedagogical content knowledge, which is a complex knowledge base for teaching (Shulman, 1987). Janík (Janík, 2007), basing his findings on Shulman, presents a cycle of pedagogical reasoning and action. This cycle can be applied to museum didactics as well:

Comprehension – the teacher is very well acquainted with what he teaches.

591

Jiří Hoffman

Transformation – the teacher transforms the educational content into a comprehensible form for the students. This process consists of several steps:

Preparation – analysis, interpretation and breaking the educational content into sections in accordance with the educational objectives.

Representation – what tools are suitable for presenting the educational content.

Selection – arranging the educational content into suitable educational forms and methods.

Adapting the educational content to the students – based on students' age, skills and the amount of motivation.

Instruction – suitable activities in the lesson (classroom techniques, presenting the educational content, interaction with students, asking questions etc.)

Assessment – using tests in the course of the lesson as well as at the end.

Reflection – later assessment of the conducted lesson.

New comprehension – based on gained knowledge the teacher adjusts the next lesson.

2.5 Museum education target groups There are many various user groups coming to the museum. Talboys (Talboys, 2005) divides them into following categories: preschoolers, children and teenagers, college and university students, future teachers, teachers, adult visitors, specific groups, disabled and casual visitors. Systematic teaching can be applied to one of these groups. The largest group consists of primary and secondary school students. This article deals with preparation of such a group for a visit to a museum. A visit to an institution such as museum can be educational as well as entertaining. The learning process during such extracurricular activities can be divided into three parts:

1st phase – theoretical preparation in school;

2nd phase – visit to a museum, the experiment;

3rd phase – going back to school, evaluation, feedback.

The author aims to gradually build an entire chain of methodology for the specific field of extracurricular education. There is a plethora of new opportunities and new available media that can be taken advantage of.

3. Preparation of pedagogical experiment While planning the experiment, the author considered various ways of preparing the students for the experiment. After examining all the pros and cons, he selected the option of an online e‐learning course primarily because of the amount of time needed for independent study done by the student.

3.1 Experiment goal and research questions The goal of this experiment is to verify whether education using the physical handling of exhibits enables students to remember more and enhances their motivation beyond that of regular classroom education (Andromedia.cz, 2012). The experiment will be held before the end of school year 2012/2013. Research questions:

What is the influence of 3D exhibits and interactive aids when acquiring knowledge?

Will the experiment yield better results than regular classroom education?

Will such a form of education be more interesting for the students?

The research will compare the amount of acquired knowledge retained in classical education with that gained in a lesson conducted in a museum using exhibits.

592

Jiří Hoffman

3.2 E‐learning module The lesson, an e‐learning module, is specifically designed to prepare the students for the lesson in the technological museum (preparation phase of the experiment). E‐learning form was selected in order to allow the students to prepare comfortably from their own home in compliance with their own individual learning needs. The module aims to help the students get acquainted with the content of the lesson in the upcoming experiment. The module is based on Instructional System Design (ISD). It was designed based on a museum exhibition content and an analysis of instruction requirements for 7th grade students of a primary school. The content of the lesson and the scope of the curriculum was consulted and approved by a primary school teacher. The e‐learning module is available as a course in Learning Management System (LMS) Moodle which is used in primary and secondary schools [9]. The course is intended for independent study prior to the experiment. This study takes place at student's home and the student himself can determine the amount of information he wishes to acquire. It is vital that the information provided in the course are comprehensible, compendious, suitably worded and corresponding with the requirements of the curriculum scope for primary schools. The course offers three levels of information:

1st level – consists of basic and brief information. The textual information is arranged in bullets and contains basic terminology and formulas.

2nd level – Presented information is arranged in simple sentences. The scope of the textual information equals information available in an appropriate primary school textbook. Included are computational formulas and samples of word math problems solutions. The textual information presented in this level does not exceed the expected level of knowledge for primary schools.

3rd level – The textual information is consistent with content of a primary school textbook. Available are samples of word math problems solutions. This level is enhanced by additional information, such as interesting historical facts or current practical implications. Moreover, internet links to additional information about the lesson content are provided.

The course is only available for one week before the initiation of the pedagogical experiment. The student does not have any other way of preparation, other than the e‐learning course, since the discussed curriculum is taught later in a higher grade of a primary school. Without passing the course, the student lacks the basic knowledge necessary to successful completion of the experiment (?). At the end, the course includes a set of checking questions which allow the student to revise the presented content.

3.3 Interactive exhibition U6 The paper deals with the preparation of pedagogical research that is a pedagogical experiment. This experiment will take place in a technological museum called U6. The exhibition is situated in a unique building of the former Energy Central Station. This building is located on the grounds of the former industrial center for the production and processing of iron in Ostrava in the Czech Republic. A small science and technology center called The Small World of Technology is part of the building. The museum holds a permanent interactive techno‐historical exhibition. The theme of the exhibition was inspired by the atmosphere of adventurous books written by Jules Verne. The content of the exhibition corresponds with the industrial history of the city of Ostrava and is focused on the manufacturing and processing of iron. Other topics include the industrial revolution, steam engines, the generation of electricity, acoustics and oscillation, water turbines, aeronautics and astronautics. The design of the exhibition allows for the physical handling of the exhibits. Among the items in the mechanical exhibits that the visitors can touch are mechanical models, levers, pulleys, models of the steel processing, and motorcycle, automobile or aviation simulators. The most attractive exhibits are a MIG 21 jet engine, a locomotive engine room or an imitation of the Nautilus submarine.

593

Jiří Hoffman

3.4 Preparation of the experiment The experiment uses the customary approach of testing two different groups (experimental and control group). The lesson for the experimental group is designed to take place in the technological museum. Students are able to physically handle the mechanical exhibits. The lesson of the control group is designed for a classroom in The Small World of Technology educational center.

3.5 Lesson content The topic of the lesson is so‐called Simple Machines. There are various types of pulleys and levers available for hands‐on experimentation. The topic Simple Machines is taught as a part of physics for 8th grade in a primary school. For this experiment, students of 7th grade (age 12 to 13 years) were chosen, because the school year is nearly over and since they are not yet acquainted with the topic. Experimental lesson – is based on physical experience with the exhibit. Students can freely test various pulleys and levers using their own weight (experiments with lifting their own weight). Furthermore, the museum provides a data projector, screen for the teacher’s presentation as well as a flip chart for example calculations. Chairs are placed in such a way among the exhibits that students are able to see the practical demonstrations during the lesson. Mechanical exhibits – pulleys and levers – are available. The lesson is conducted through animation with practical demonstrations. It contains:

introduction to and the history of the origins and use of simple machines,

theoretical commentary and discussion with students,

viewing and demonstrative testing of exhibits,

practical follow‐up activity (in groups),

final revision,

test and a questionnaire.

Regular lesson – is conducted as a usual lesson without the use of physical aids. The classroom is equipped with a data projector, screen for the teacher’s presentation as well as a flip chart for example calculations. No exhibits or physical aids are available during the lesson. The lesson is conducted as a regular lecture. It contains:

introduction to and the history of the origins and use of simple machines,

theoretical commentary and discussion with students,

presentation of pictures of simple machines,

theoretical discussion (in groups),

final revision,

test and a questionnaire.

3.6 The process of the experiment Division into groups – Students will be separated into two groups using the method of simple random sampling (drawing numbers). Every member of each group is assigned a unique identification number. Lesson process – To ensure the same conditions for testing, the same teacher presents the topic to both groups. First, the lesson with the experimental group and available exhibits is conducted. Meanwhile the control group watches a 3D documentary film. After the lesson the experimental group fills out the final test in an unoccupied classroom. When they are done, the groups switch. The experimental group watches the 3D documentary film, while the control group has a regular lesson with the teacher. After the lesson the control group fills out the final test. The whole experiment lasts 2,5 hours.

594

Jiří Hoffman Conclusion – After completing the experiment, all students fill out a questionnaire and the final test. The questionnaire is focused on the following areas: quality and attractiveness of the lesson, pros and cons of the lesson, the final test verifies the growth of students' knowledge.

Test – The lesson content is aimed at understanding facts and procedures?. The final test serves for knowledge evaluation of the basic facts (examples of simple machines, pulleys, levers, computational formulas, definitions etc.) and procedures (the principles of how simple machines work, computation of word math problems, suitable practical implications in life, ways of using simple machines in the history).

The final test contains various types of questions: single choice answer, answer using student's own words, embedded answers, description. The test is created by the author of the experiment. All testing questions are consulted and approved by a primary school teacher.

Questionnaire – The final questionnaire is designed to determine the satisfaction of the students with the experiment. Students from both groups (experimental and control) express their own opinion about the content and course of the experiment. The students evaluate: attractiveness, entertainment value, quality, elaboration, teaching style, comprehensibility, lucidity, clarity of instruction. The evaluation is based on common school grading (1 – excellent, 5 – poor).

Other questions include students' impressions about the lesson, the most and the least interesting parts of the experiment, interest in another experimental lesson, meaningfulness and effectiveness of another similar experimental lesson, etc. The questionnaire is designed by the author of the experiment and is consulted and approved by a primary school teacher.

At the end of the experiment, all participants get to tour the whole museum exhibition with a guide (as a sort of a reward).

3.7 Assessment of the preparation of the experiment Given the original expectations of the experiment, only 32 students from the 7th grade of a primary school attended the experiment. The questionnaire survey was processed on the first two levels of Kirkpatrick's Four‐ Level Training Evaluation Model (Kirkpatrick's Four‐Level Training Evaluation Model, 2013). Work on the experiment and further evaluation is ongoing. 1st level: Reaction – Evaluation took place immediately after the experiment. The questionnaire was distributed to the respondents. It was designed to determine the level of the students' satisfaction with the form and the content of the lesson, the quality of the instructor and the environment where the experiment took place. The experimental group (1 = excellent, 5 = poor)

The overall course of the lesson: 1 (56 %), 2 (31%), 3 (13 %), 4 (0 %), 5 (0 %).

Teaching style: 1 (81 %), 2 (7 %), 3 (13 %), 4 (0 %), 5 (0 %).

Attractiveness and entertainment value: “definitely yes” (19 %), “yes” (81 %).

The best part of the lesson was “the possibility to handle the physical exhibits” (100 %).

87 % of the students are satisfied with the lesson content and would not make any changes, the rest of the students would like to have “less talk and more practical testing of the exhibits” (13 %).

100 % of the students consider the lesson complete and worthwhile.

The students see the value of the future use of the same type of instruction “definitely yes” (32 %), “yes” (68 %).

100 % of students cannot find anything they dislike in the experiment.

Students evaluate whether they acquired more information in this lesson than in school, “yes” (81 %), “no” (19 %).

The overall rating of the experiment on the scale 1 – 10 (1 = poor, 10 = excellent): 10 (38 %), 9 (44 %), 8 (6 %), 7 (6 %), 5 (6 %).

595

Jiří Hoffman The control group (1 = excellent, 5 = poor)

The overall course of the lesson:1 (43%), 2 (51%), 3 (6 %), 4 (0 %), 5 (0 %).

Teaching style: 1 (68 %), 2 (18 %), 3 (12 %), 4 (0 %), 5 (0 %).

Attractiveness and entertainment value: “yes” (75 %), “no” (25 %).

The best part of the lesson was “the film” (75 %), “the lecture” (25 %).

62 % of students are satisfied with the lesson content and would not make any changes, the rest of the students consider it boring (38 %).

37 % of students consider the lesson complete and worthwhile, 63 % miss a practical demonstration or experiment.

The students see the value of the future use of the same type of instruction “yes” (100 %).

68 % of students cannot find anything they dislike in the experiment, 32 % answered “I do not know.”

Students evaluate whether they acquired more information in this lesson than in school, “yes” (81 %), “no” (19 %).

The overall rating of the experiment on the scale 1 – 10 (1 = poor, 10 = excellent): 10 (25 %), 9 (57 %), 8 (6 %), 5 (6 %), 4 (6 %). nd

2 level: Learning – The final test evaluates the amount of acquired knowledge during the learning process. The final evaluation was very similar when comparing both groups. The whole control group received 206 points. There was a significant difference between the levels of acquired knowledge of individual students in this group. The student with the highest score received the full amount of points (22/22 b), but the student with the lowest score received only 4 points (4/22 b). The number of points are spread out over almost the entire rating scale. The whole experimental group received 203 points, but the difference between the highest (19/22 b) and the lowest (9/22 b) score was not as significant. The overall score is more consistent than in the control group.

4. Conclusion Conducting a lesson in a museum requires careful preparation. The teacher needs to be acquainted with the topic of the lesson as well as the content of the exhibition in order to organize the lesson in a suitable way. Of a great importance is the interconnection of relations between the visitor, teacher and the presented exhibit. A lesson conducted in such a way is then interactive and entertaining and allows students to learn by physical experience. When preparing the lesson, the teacher can either take advantage of the museum's educational strategies or create his own. The lesson needs to be tailored for the specific target group and also needs to take into consideration the educational resources of the museum. Even though a museum is not primarily intended for conducting lessons, it is considered an educational institution which can allow for an interesting alternative to a regular classroom education. The survey results suggest that the students view the experimental lesson as more entertaining and interesting than a regular lesson. The students rated positively the possibility of experiencing physical experiments in practice. Although the survey failed to demonstrate that the use of experimental teaching has brought a greater amount of acquired knowledge than traditional forms of teaching, this fact can be attributed to the low number of students tested and therefore it is necessary to take the resulting values rather as a rule of thumb. To date, this has been the first experiment. Based on the results of the evaluation, changes will be made to the test questions and the questionnaires will be given more attention. In the future more primary schools will be involved and the experiment will continue and gradually expand.

References Andromedia.cz (2012) Pedagogický experiment (Pedagogical experiment). [online] Avaliable at: http://www.andromedia.cz/andragogicky‐slovnik/pedagogicky‐experiment [Accessed: 29 May 2013].

596

Jiří Hoffman Edcom.org (2000) AAM Education Professional Network. [online] Available at: http://www.edcom.org/ [Accessed: 29 May 2013]. Educational Review, Available at: http://people.ucsc.edu/~ktellez/shulman.pdf [Accessed: 21 May 2013]. Kirkpatrick's Four‐Level Training Evaluation Model (2013) Analyzing Training Effectiveness. [online] Avaliable at: http://www.mindtools.com/pages/article/kirkpatrick.htm / [Accessed: 21 May 2013]. LondonMuseums.org (2011) Strategy. [online] Avaliable at: http://www.londonmuseums.org/strategy.htm [Accessed: 29 May 2013]. Hooper‐Greenhill, E. (1991) "Museum and Gallery Education. Leicester", Leicester University Press, London and New York. Moodle.org (2013) Moodle.org: About. [online] Available at: https://moodle.org/about/ [Accessed: 21 May 2013]. Instructional Design Models (2013). [online] Avaliable at: http://www.personal.psu.edu/users/w/x/wxh139/ISD_talk.htm [Accessed: 21 May 2013]. Instructional Design Central (2012) Instructional Design Models, ADDIE Model. [online] Available at: http://www.instructionaldesigncentral.com/htm/IDC_instructionaldesignmodels.htm [Accessed: 29 May 2013]. Janík, T. (2004), "Pedagogical content knowledge nebo didaktická znalost obsahu?", Paido, Brno. Průcha, J. (2000) "Přehled pedagogiky: Úvod do studia oboru" (Overview of Education: Introduction to the field of study), Portál, Praha. Šobáňová, P. (2012) "Muzejní edukace" (Museum education), Univerzita Palackého v Olomouci, Olomouc. Talboys, G. (2005) "Museum educator's handbook", Ashgate Pub. Co., Burlington.

597

Machine and Social Intelligent Peer‐Assessment Systems for Assessing Large Student Populations in Massive Open Online Education Cristian Jimenez‐Romero1, Jeffrey Johnson1 and Ricardo De Castro2 The Open University, Milton Keynes, UK 2 Corporación Universitaria Reformada, Barranquilla, Colombia

cristian.jimenez‐romero@my.open.ac.uk jeff.johnson@open.ac.uk rcastro@unireformada.edu.co Abstract: The European Étoile project aims to create high quality free open education in complex systems science, including quality assured certification. Universities and colleges worldwide increasingly use online platforms to offer courses open to the public. Massive Open Online Courses (MOOCs) give millions of people access to education from prestigious universities. Although some courses provide certification of attendance and completion, most do not provide any academic or professional recognition since this would imply a rigorous and complete evaluation of the student’s achievements. Since the number of students enrolled may exceed tens of thousands, it is impractical for a lecturer (or group of lecturers) to evaluate all students using conventional hand marking. To be scalable, assessment must be automated. State‐of‐the‐art automated assessment includes multiple choice questions and intelligent marking techniques (involving complex semantic analysis). However, none of these alone can cope with very large student populations of students and guarantee the evaluation quality required for higher education. The goal of this research is to create and evaluate a computer mediated social interaction system for massive online learning communities. This must be scalable and able to assess fairly and accurately student coursework and examinations. We call this approach “machine and socially intelligent peer assessment”. We describe our system and illustrate its application. It combines peer assessment and reputation systems to provide independent computerised assessment. Assignment of student markers to scripts is based on reputation scores which emerge from their marking behaviour. A simulation experiment shows how reputation‐based social structure evolves in our peer marking system. A pilot experiment with ninety 16‐year old high school students in Colombia tested the marking accuracy of our system by comparing the statistical differences between teacher‐marked ‘gold standard’ scores, peer assessment using average scores, and our intelligent reputation‐based peer assessment. The research question is to what extent does the proposed approach improve peer marking in terms of marking accuracy and fairness? We report the first results of this experiment, summarise the lessons learned, and describe further work. Keywords: MOOCs, automated marking, peer assessment, reputation systems, complex systems education, Étoile

1. Introduction The European Étoile project aims to create high quality free open education in complex systems science, including quality assured certification (http://www.etoileplatform.net/index). In the first instance we are focused on providing postgraduate education for master and doctoral students and other researchers. This means that, although we can assume that our students will be well motivated with good study skills, we share the same general challenges of open online learning. Massive Open Online Education is gaining not only traction but legitimacy through the offer of open online courses given by prestigious academic institutions around the world. Academics from Harvard, Stanford, MIT, the Santa Fe Institute, and many others institutions are giving online lectures in many subjects to millions of students. Access to these lectures is in most cases without any academic restriction and without any fee. Massive Open Online Courses (MOOCs) are mostly found in dedicated online education platforms (e.g. Coursera), which offer an extensive curriculum of courses in a variety of subjects including mathematics, computer science, natural sciences and social sciences. Giving massive and ubiquitous access to high level education provides a valuable contribution to the expansion of knowledge in society, especially in poorer regions of the world where a large part of the population does not have access to university level education. Thanks to their content, quality and accessibility, MOOCs allows students from everywhere to gain significant knowledge and skills for personal and professional development. However, the question is to what extent is it

598

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro possible to get academic and professional recognition from a MOOC? The answer concerning academic accreditation is very limited. Some courses offer a certificate of completion, in some cases giving a record of the student performance and proof of participating in the course, but do not provide any university credits that can be transferred to a higher education qualification. Granting academic recognition in a MOOC would imply a rigorous and complete evaluation of the student’s achievements. Taking into account the large number of students enrolled in a course which often exceeds tens of thousands, it is impractical to evaluate all students using conventional hand marking. Thus in order to be scalable, assessment must be automated. The absence of academic accreditation of MOOCs represents an issue of accessibility to widespread of certified higher education. We propose in this paper an alternative to overcome this barrier through the design of a model capable of handling large student populations while producing high quality assessment. We implemented the algorithms described in our model in a computerised assessment system and evaluated this in an experimental study performed with ninety 16 year‐old students in a secondary school and also in a simulated scenario with larger populations of students.

2. Automated assessment State‐of‐the‐art automated assessment includes various methods and computerised tools such as multiple choice questions and intelligent marking techniques (involving complex semantic analysis). However, none satisfy all the requirements of an assessment system able to cope with very large populations of students also able to guarantee the quality of evaluation required for certified higher education. Exclusive use of any single format for assessment is not recommended (American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education, 1999). Multiple choice questions One of the most commonly used assessment formats is multiple choice testing (Haladyna, 1999; McDougall, 1997). This mature and well known assessment method is recognized as an efficient way to evaluate a multidisciplinary range of knowledge (Haladyna, 1999). Once a multiple choice questionnaire has been created, it is possible to use computational tools for assessment of large student populations. However, there are significant drawbacks:

multiple‐choice assessment is not suitable to evaluate all types of knowledge.

testing student understanding beyond the superficial can require great ingenuity

multiple choice questions can be time‐consuming to prepare.

questions can be ambiguous (but statistical methods make this easy to detect and correct).

students are not able to demonstrate partial knowledge.

students may gain marks by chance, spuriously indicating knowledge and learning

completing many multiple choice questions can be repetitive, boring and demotivating.

Multiple choice assessment is a useful evaluation method when combined with another assessment formats (e.g. open questions) but using it exclusively is not enough to guarantee complete and accurate knowledge evaluation. Intelligent and Short answer marking

This method refers to open questions with free text entry where the student has to write an answer usually no longer than four sentences. Here the student constructs rather selects answers (www.jisc.ac.uk, 2013). The aim is to obtain in the shortest possible text a definition or a set of facts. Short answers can then be evaluated by a computerised interpreter which may perform syntactic and semantic analyses of the text. The ability and accuracy of these interpreters varies according to the technical complexity of the engines. Some use algorithmic manipulation of keywords while others use complex computational linguistic techniques to perform spell checking, syntax normalization, morphologic analysis, pronoun resolution and many others levels of linguistic processing (Butcher et al, 2010). The limited amount of words permitted by the interpreters

599

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro does not allow students to create larger essays which may be necessary for more complex questions. Moreover, marking engines may not perform well for questions with an unpredictable range of valid answers e.g. ‘what is freedom?’ (www.jisc.ac.uk/media/documents/projects/shorttext.pdf (April 2013). Specialized commercial assessment software uses model essays (pre‐graded scripts) as templates to compare aspects such as style, vocabulary, length, etc., e.g. ETS. However, computerised tools are not really “aware” of the items or subjects they evaluate.

Short answer marking can be very effective and can provide students with hints or further remedial material if at first they get the answer wrong, e.g., an incorrect response can generate automatic feedback telling the student to read a particular page of text, view a video, or follow a link directing them to material enabling them to answer the question. In this way the student can be diagnosed as initially not knowing something, but can be rewarded for their learning by being given credit at second or subsequent attempts (Johnson et al, 2012, page 231) Peer Assessment: Peer Assessment, or Peer Marking involves students assessing each other. They may be fellow students in the same evaluated population, or students who have studied the subject being evaluated. This is an innovative strategy (McDowell and Mowl, 1996) with demonstrated success in improving student learning. Peer assessment also has a positive impact on teachers or tutors by allowing them to use their time more efficiently, and also to get the results of student assessments in a shorter time (Sadler et al, 2006). Because of these benefits, and taking into account that the size of the assessed population is equal to the number of assessors, this method offers a potential solution to the gap between MOOCs and certificated higher education. However, it is necessary to consider reliability and validity before going for a pure peer assessment approach. One of the first questions for peer assessment is its accuracy and reliability. Studies have shown that in some scenarios peer assessors tend to overrate their peers giving higher marks than the teacher would do (Falchikov, 2002; Roach, 1999). The opposite case has been also observed, where the tendency is to underrate the assessed peers (Penny and Grover, 1996 in Heywood, 2000, 387). Peer marking is widely used in schools, often with a single student peer‐marking another student’s work, even though more than one peer marker is recommended (Bostock, 2000). This has the problem that a student will be penalised (or advantaged) if their single peer‐marker gives a low (or high) score compared to the mark that would be given by a teacher. This problem may be alleviated by an argument that these discrepancies will be evened out over a number of pieces of work. Multiple peer marking addresses this by giving distributions of marks. If the markers are highly competent their marks will be similar. If the markers are less competent there will be greater variance. Thus a high variability indicates that one or more of the peer markers is giving erroneous marks. Averaging the marks reduces the error compared to the worst marker and increases the error compared to the best marker. Taking the average is a compromise that reduces the frequency of large errors made by individuals at the expense of accepting an average error for the group. In theory for any assessment there is a ‘gold standard’ of marking provided by the teacher, i.e. all teachers would give the same mark for a given piece of work, and the quality of peer marking is defined relative to this standard. In practice the marks of experts can vary considerably. At the UK Open University, courses may have thousands of students assessed by teams of markers. Quality assurance procedures involve systematic sampled second marking. While most second marking confirms the original mark, sometimes there are variations outside a limit of 15% which identifies weak markers and triggers third marking. This outside the scope of this paper. In our experiment all student answers were marked by a single teacher and our results are relative to the quality and consistency of his marking.

600

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro Improving the quality of Peer assessment Since the abilities and performance of peer markers vary, it is necessary to find mechanisms to reduce the error resulting from using groups of assessors. One area of focus has been achieving objectivity. Several mechanisms have been proposed for eliciting Honest Feedback (Prelec, 2004; N. Miller, P. Resnick, and R. Zeckhauser, 2005), including the Peer Prediction method (Miller et al, 2005) where honest reporting between peer assessors is sustained by a Nash equilibrium. Another mechanism proposed by Hütter, C.; Kimmerle, T. & Böhm, K. (2012) is to reward assessors according to their feedback quality. Increasing honest feedback in a peer assessor population may have a significant impact on the quality of scores. However, truthful or honest marking is only one of the factors affecting accurate marking. Highly deviant marking can occur for other reasons, e.g. lack of knowledge about the evaluated question‐answer or misunderstanding of the assessment criteria. Thus, having different factors affecting marking behaviour requires mechanisms to identify and quantify the marking accuracy of each assessor. Probabilistic frameworks (Witkowski, J. & Parkes, D. C., 2012; Carpenter, 2008) using Bayesian inference have been proposed as a strategy to predict the gold standard and classify assessors according to their score quality, by assigning weights to them.

Like Witkowski et al (2012), we use a weighting mechanism according to the calculated performance of each assessor. However, our system is simpler with less equations. Also our system automatically assigns assessors to the assessed population based on a probabilistic distribution so that each assessed individual has an equal chance to be evaluated by a group of poor and good assessors.

3. The hypothesis being tested Our approach gives peer markers a reputation score that discriminates the better and weaker markers. Weak marking can be systematic, e.g. always too high or always too low. This can be seen as a calibration problem and in principle can be detected by its systematic nature. In contrasts some weak markers give inconsistent marks with work of the same quality being given different marks. Our system is based on the underlying hypothesis that peer markers giving similar scores are more likely to be good markers than peer markers giving deviant scores. On this basis the reputation of each marker can be recalculated after each session, as explained in Section 5. Hypothesis The aggregate mark given to an answer by triples of peer markers on the basis of dynamically calculated reputation and marker selection will be closer to the gold standard mark than the average score of triple marking with no use of reputation information.

4. Description of experiment To evaluate our approach, we conducted a study with 90 students and one teacher over eight weeks. One control group and one experimental group were created. Each group has 45 students selected at random. Each student was assigned a unique code which was used to identify them within this study. The students had four hours of classes per week. The first hour on the first day was used for teaching. The next day half of the second hour was used for students to complete new tests, and half an hour used to peer mark the answers from a previous session. On the third day the hour was used for teaching, and on the fourth day the fourth hour was again used for answering questions (half an hour) and peer‐marking answers from the previous session. In four weeks, it was possible to get seven samples of peer marking. For the control group, each copy is randomly distributed to the control population (excluding the student whose exam is being distributed since self assessment is outside the scope of this study). Once an exam has been peer reviewed, the score is calculated based on the average from the score given by the 3 peer assessors. For the experimental group, the identification code of all students being examined is given to our system. This generates a list for each exam script with the code of its 3 peer assessors. Once an exam has been peer reviewed, the score given by each peer assessor is entered in the system. This calculates the exam score based on the weighted sum of the peers’ reputation.

601

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro During the first month of study, students were assessed and peer‐assessed twice a week for a total of 560 marked exams, equivalent to 1680 peer reviews. To analyse the accuracy of peer assessment, the teacher marked all exams as the ‘gold standard’. We are comparing the differences between the scores calculated in the control and the experimental group with the scores given by the teacher. The experiment was done in a (Spanish) linguistics lesson corresponding to what is called English Language in the UK, which includes elements of spelling, grammar, comprehension, forming shorter abstracts of text (precise), and general concepts of literature. An illustrative question is: Explique como se crea un guion tecnico y los pasos a seguir which translates into English as Explain how to create a technical script (in a theatre) and the steps to follow. The students’ answers to the questions had two sheets of paper. The first sheet had their name and other information. The second sheet included the question and their answer. The preparation for each lesson included making three photocopies of the question‐answer sheets for the previous lesson with hand written codes on each identifying the student and one of the three selected peer markers for this students’ answer. The section algorithm usually assigned different peer markers to each student’s answers. In the second thirty minutes the students were given three scripts to peer mark, these being answers given by their peers in the previous lesson. Students were motivated to give good answers since the gold standard assessment of the teacher is used to grade the course. They were motivated to peer assess other students well by being told that their performance would also count to the overall assessment of the course. This paper covers the first four weeks of the experiment. In the first week the process of answering questions and peer marking was explained to the students. The college studied has a high reputation in its city and its students are highly motivated with relatively good academic achievements, and the students all complied with the procedures and tried to give good peer assessments.

5. The experimental peer‐assessment system The system has three components: Management of students’ data: The system uses a reputation score on a scale of 1 to 100. There are two types of reputation: one for the role as assessor and one for the assessed role. The assessor reputation score reflects the individual as a peer evaluator. The assessed score measures the performance of the individual as a student based on the results of one or more assessments. Both types of scores are subject specific, allowing independent measures in both assessor and student roles for each specific subject. For instance, an individual may have a higher reputation as assessor in mathematics, a lower score as assessor in history, but a high score as a student in these subjects. Student may be associated with several subjects depending on the courses they study. This is supported in our system by a many‐to‐many relationship between the student and subject entities. Selection of Peer assessors for marking On completion of an examination, the system generates for each given answer a pool of n (3 in our system) assessors. This pool is selected from the assessor population which has been previously associated to the subject of the question/answer.

602

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro To achieve the fairest assessment, the pool of assessors is created by probabilistic selection based on the reputation score of each assessor. The objective is for every student to have the same probability of being assessed by at least one higher‐reputation marker, one lower‐reputation marker and one random marker when the pool size is of three or larger. In order to achieve this, each assessor is associated with a variable known as selection preference Sp which is initialized with the reputation value of the corresponding individual. Once, an individual is selected to be an assessor for a pool, their Sp variable is assigned a value equal to the average of the reputation of the entire population associated with the subject of the evaluated answer. This reduces the probability of the same individual being selected immediately afterwards as a high/low‐reputation assessor, thus preventing an overload of work and also giving other individuals the opportunity to become assessors in a pool. Recovering preference: each time a new pool of assessors is selected, the Sp variable of each individual i tends to reach the value of their corresponding current reputation Rp . Thus, the probability of being selected as a high/low marker grows over time to a value proportional to the individual's current reputation. The growth over time of Sp is indicated by a recovery rate Rcr (1.0 in our system). The recovery process

⎧⎪Spi + Rcr , if of Spi = ⎨ ⎪⎩ Spi − Rcr , if

Spi < Rpi

Spi > Rpi

The mechanisms for selection of random and high/low reputation assessors are defined as follows: High Reputation assessor: for the selection of this marker the "Fitness proportionate selection" algorithm is used. Such ‘Roulette wheel selection’ is widely used in genetic algorithms. This mechanism allows the reputation of each potential peer‐assessor to be associated with their probability of selection. If fi is the reputation of individual i in the assessors population (associated to the question), with f i = reputationi , its probability to be selected in the markers pool is

pi =

∑ j =1 f j N

, where N is the number of individuals in

the pool population (1) When an individual i is selected, this is locked by the system and they cannot be selected again for the same assessor pool, thus avoiding the issue of repeated assessors for the same student answer. Low Reputation assessor: This individual is also selected based on the Fitness proportionate selection mechanism. However, the fitness fi is defined as the difference between the maximum scale value (100 in our system) and the reputation of individual i in the assessor population. f i = Max.ScaleValue − reputationi , with a probability to be selected given by:

pi =

∑ j =1 MaxScaleValue − f j N

(2)

This low reputation assessor also remains locked until the pool population has been totally formed. Random assessor: this individual is randomly selected from the non‐locked assessors in the population associated to the subject of the question. Weighted Score Calculation:

603

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro The score of each answer is calculated on a scale from 1 to 100 based on the weighted sum of the scores given by the peers in the pool. The weight w of the score s of an assessor i depends on their reputation r and is defined as wi =

∑

i M

, where M is the number of individuals in the assessors‐pool population. (3)

r j =1 j

Thus, the final score S c of the answer is expressed as:

S c = ∑ wi si

(4)

i =1

The following example illustrates this process. Suppose the following pool of three peers has been selected to grade a student answer. Peer 1 Peer 2 Peer 3 Assessor Reputation 45 22 71 Given score 70 68 40

The weights of each peer are:

Peer1w =

45 22 71 = 0.33 , Peer 2 w = = 0.16 , Peer3 w = = 0.51 45 + 22 + 71 45 + 22 + 71 45 + 22 + 71

Applying the formula (4) with the above calculated weights the final answer score is: S c = 0.33 × 70 + 0.16 × 68 + 0.51× 40 = 54.38 Adjusting the reputation score: Once the score of an answer has been calculated, the next step is to adjust the reputation scores of each pool assessor. To do this, the difference Δsc i = sc i − Psc , between the score sc given by each assessor i and the calculated pool score

Psc is taken as the criterion to determine if the given score falls within a given

acceptance range Thr (15 in our system):

⎧⎪W+ , if Wi = ⎨ ⎪⎩W− , if

Thr ≥ Δsci

(5)

Thi < Δsci

where Wi represents the weight to be subtracted ( w− ) or added ( w+ ) to the current reputation score of the assessor i . In case Δsci falls within the range Thr the positive weight w+ is calculated as follows:

wi + =

1 x (Δrpi + 1) 5 × learning p (Δsci + Thr × 0.5)

(6)

where Δrpi = Rpi − BRp is the difference between the reputation Rp of assessor i and the highest reputation value BRp in the pool. learning p is a constant which determines the increase factor of the reputation (we used values between 1 and 10). In case Δsci falls outside the range Thr the negative weight w− is calculated as:

wi − = −

(Δsci + Thr × 0.5) x (Δrpi + 1) 10 × learning n Maxsc

604

(7)

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro where Maxsc is the maximum scale value (100). learning n is a constant which determines the decrease factor of the reputation. Knowing Wi from formulas 5, 6 and 7 the new reputation Rp of assessor i is adjusted as follows:

Rpi = Rpi + Wi

(8)

6. Results Table 1 shows the experimental peer marking results for sessions 5, 6 and 7, e.g. in the leftmost column (Session 5) Student‐1 was assessed at 62.75% by the three peer markers and 60% by the teacher (2.75% difference), while Student‐2 was assessed by the three peer markers as 61.75 compared to 40% by the teacher (20% difference). The columns marked Error show the differences between the combined peer marker scores and the teacher’s score. At the bottom of these columns is the mean error, 12.43% for Session 5, 10,31% for Session 6 and 9.3 % for Session 7. Table 1: Experimental peer marking results for sessions 5, 6 and 7

605

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro Our findings in Table 2 reinforce previous research that shows peer marking improves as students become more experienced (Hanrahan, S.J. & Isaacs, G. (2001)). These data, displayed in Figure 1, show that the mean difference between the peer marking and the teacher decrease over time. The first two sessions are shown against a grey background to indicate that the values for the reputation‐based experimental marking are arbitrary since the initial reputations are not formed or stable. Table 2: Mean errors for experimental and control cohorts

Session 1 Session2 Session 3 Session 4 Session 5 Session 6 Session 7

Control

42%

32%

23%

14%

15%

11%

13%

Experimental

31%

29%

20%

13%

12%

10%

Mean deviation from teacher’s gold standard mark

error for control cohort not using reputation error for experimental cohort using reputation

weeks

Figure 1: The mean errors for the reputation‐based scores are lower than the average‐based scores Figure 1 gives a summary of our results and shows that the reputation‐based system gives results about 2% ‐ 5% closer to the gold standard than taking averages. Statistical tests (Table 3) suggest that this effect may not be significant for the first few sessions while the students are learning how to do peer marking, but by sessions 5, 6 and 7 they are unlikely to have occurred by chance. Thus the original hypothesis has been validated by this study, suggesting reputation‐based systems may give a small benefit in performance compared to score‐ averaging systems. Table 3: An analysis of variance suggests that the control and experimental mean errors are significantly difference for the last three sessions

7. Discussion These results suggest that our reputation‐based peer marking can give aggregate results within 9% of the teachers’ mark. In principle 9% difference is sufficiently close to conclude that peer marking is almost as good as the ‘gold standard’. However care must be taken with such an interpretation. Whereas by the final session the mean difference reduced to 9%, inspection of the rightmost column in Table 1 shows a high proportion, 6 out of 34, where the differences were above 15%, a value that would trigger remarking at the Open University. Thus, as it stands, our reputation‐based peer marking system has not yet been demonstrated to deliver the high quality marking that we require.

606

Cristian Jimenez‐Romero, Jeffrey Johnson and Ricardo De Castro Computer simulation experiments of our reputation‐based system suggest that the errors continue to reduce after eight or more sessions. Since the experiment is continuing beyond the seven sessions reported here, we will soon be able to test this empirically. Apart from these expected improvements we intend to make further developments to the system. For example, asking students to provide feedback on their assessment, saying whether or not they felt it was accurate. This will provide further data for the peer marker reputations. Also currently we do not use the time series of marker differences implicit in the data, and these may also help to discriminate strong and weak peer markers. In summary we consider this experiment to give a strong indication that our reputation‐based peer marking can be developed to give the robust high quality assessment required for accreditation.

References American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. Standards for educational and psychological testing. Washington, DC: AERA, 1999 Bostock, S. (2000). Computer‐Assisted Assessments—Experiments in Three Courses. From Learning Technology website, Keele University. (Last Accessed: 25 May 2013). Boud, D. and Holmes, H. Self and peer marking in a large technical subject, 63‐78 in Boud, D. Enhancing Learning through Self Assessment, London: Kogan Page, 1995. Butcher, P. G., Jordan, S. E., ‘A comparison of human and computer marking of short free text student responses’, Computers & Education, Volume 55, Issue 2, Pages 489‐499, September 2010 Carpenter. Multilevel bayesian models of categorical data annotation. Technical Report available at http://lingpipe‐ blog.com/lingpipe‐white‐papers/, 2008. Falchikov, N. (2002). ‘Unpacking’ Peer Assessment’. In P. Schwartz & G. Webb (Eds.). Assessment: Case Studies, Experience & Practice from Higher Education. London: Kogan Page. Haladyna, T. M. Developing and validating multiple choice test items (2nd Ed.). Mahwah, NJ: Lawrence Erlbaum Associates, 1999. Hanrahan, S.J. & Isaacs, G.. ‘Assessing Self‐ and Peer‐assessment: the Students’ Views’. Higher Education and Development, Vol. 20, No. 1, pp. 53–70, 2001. Heywood, J. 2000 Assessment in Higher Education, London: Jessica Kingsley Publishers Hütter, C.; Kimmerle, T. & Böhm, K. (2012), Peer‐Supervised Learning with Built‐In Quality Control Based on Multiple‐ Choice Questions: A Case Study., in Carlo Giovannella; Demetrios G. Sampson & Ignacio Aedo, ed., 'ICALT' , IEEE, , pp. 453‐457. Johnson J., Buckingham Shum, S., Willis, A., Bishop, S., Zamenopoulos, T., Swithenby, S., MacKay, R., Merali, Y., Lorincz, A., Costea, C., Bourgine, P., Louçã, J., Kapenieks, A., Kelley, P., Caird, S., Bromley, J., R. Deakin Crick, R., Goldspink, C., Collet, P., Carbone, A., Helbing, D., ‘The FuturICT Education Accelerator’, Eur. Phys. J. Special Topics 214, 215–243, 2012. McDougall, D. College faculty's use of objective tests: State‐of‐the‐practice versus state‐of‐the‐art. Journal of Research and Development in Education, 30(3), 183‐193, 1997. McDowell, L. and Mowl, G., Innovative assessment ‐ its impact on students, 131‐147 in Gibbs, G. (ed.) Improving student learning through assessment and evaluation, Oxford: The Oxford Centre for Staff Development, 1996. Miller, N., P. Resnick, and R. Zeckhauser, “Eliciting Informative Feedback: The Peer‐Prediction Method,” Management Science, vol. 51, no. 9, pp. 1359–1373, 2005. Prelec D., “A Bayesian Truth Serum for Subjective Data,” Science, vol. 306, no. 5695, p. 462, 2004. Roach, P. (1999). ‘Using Peer Assessment and Self‐Assessment for the First Time’. In Assessment Matters in Higher Education. Buckingham [England]; Philadelphia, PA: Society for Research into Higher Education & Open University Press. pp. 191–201. Sadler, Philip M., and Eddie Good "The Impact of Self‐ and Peer‐Grading on Student Learning." Educational Assessment 11.(1), 1‐31, 2006. Witkowski, J. & Parkes, D. C. (2012), Peer prediction without a common prior., in Boi Faltings; Kevin Leyton‐Brown & Panos Ipeirotis, ed., 'ACM Conference on Electronic Commerce' , ACM, , pp. 964‐981

607

Virtual Guide as a Means of a Tailored Tour of an Educational Exhibition Lukas Najbrt University of Ostrava, Ostrava, Czech Republic lukas.najbrt@osu.cz Abstract: In past few years, the process of lifelong learning has become more important. A tour of an educational exposition is an interesting and attractive activity for a person undergoing education. A museum, art gallery, zoological or botanical garden or even a technological park, all these can be perceived as an educational exhibition. When we desire to tour such an exposition in the most educationally beneficial way, we can ask a guide to prepare a tailored tour according to our needs, such as depth of our interests, purpose for our tour or other provided information. There are various approaches and models for creating a tailored tour of a museum exposition. The simplest is to take advantage of a guide's services. Such a guide can even be a virtual one and can lead us through the exposition with the help of ICT equipment. This paper describes a model of such a virtual guide. Based on an initial analysis of a visitor, the virtual guide proposes a tour through the exposition so that it brings the visitor the maximal educational benefit while at the same time offers information about the displayed exhibits in such a way that is most interesting and comprehensible. The whole system consists of three modules: Visitor Module – an expert system to diagnose the visitor; Exhibit Module – a database of exhibits; Guide Module – an expert system that creates the tour, chooses and offers information about exhibits (and/or about the whole exhibition) to an individual visitor. The aim of the virtual guide is to work in the environment of the actual physical exposition. At first though, it is better to test the whole system in the environment of an online virtual museum. The testing concerns not only the functionality of the system but also the educational contribution of such a guide. Keywords: information and communication technologies (ICT), museum, virtual museum (VM), expert system, virtual guide, tour route personalization, visitor, exhibit

1. Introduction "A museum is an institution that cares for (conserves) a collection of artefacts and other objects of scientific, artistic, cultural, or historical importance and makes them available for public viewing through exhibits that may be permanent or temporary" (Alexander 2008). International Council of Museum (ICOM, established 1946, resides in Paris, it is an international professional organization with a status of UNESCO consultant) defines a museum as “a non‐profit, permanent institution in the service of society and its development, open to the public, which acquires, conserves, researches, communicates and exhibits the tangible and intangible heritage of humanity and its environment for the purposes of education, study and enjoyment”. The goal of the museum is to present a given topic in an interesting and engaging way. In order to do that, it maximizes the use of its exhibits and available technology. Likewise, it is important for the presented exposition to have an educational function. To achieve that, the exposition content has to be presented in a way that is interesting and comprehensible for the potential visitor. The range of museum visitors is wide and so it is necessary to customize the tour to some extent. The standard way to do this is a differentiation of informative texts, either as various legends to the exhibits or as printed textual guides. Another step is a personalized tour with a live guide or the introduction of audio guides, used mostly by foreign visitors. With the availability of modern information and communication technologies (ICT) museums enter a new era and the topic of personalization (customization to a particular visitor) is being discussed more often. Among the most common ICT means used in museums in the Czech Republic are information kiosks, topical interactive games, personal guides in communication devices (tablets, smartphones and communicators) or robotic guides. Virtual museums are a new domain. Thanks to the Internet, a virtual museum allows the visitor to tour the exposition right from the comfort of his home. From the beginning, there was an interest to adapt such a tour to the particular visitor. Thus, the term virtual guide came into existence. A virtual guide is software, which strives to give the visitor a tour through the virtual exposition according to his requirements. Yet another step is the logical interconnection of the real and virtual museum in one unit, the so‐called augmented museum. In this case it does not matter whether the visitor is physically present in the museum or is there only virtually.

608

Lukas Najbrt As a part of a project, I wish to create a virtual guide through an exhibition. It is to be software that is to guide the visitor through the exposition based on the characteristics given by the user when entering the museum. The virtual guide should present not only suitable exhibits, but also appropriate information related to them. The emphasis is placed on the educational benefit of the tour. After creating and testing the virtual guide in a virtual museum, the software will be transferred into a real museum with the aim of finding answers to the following questions: Question 1: Does guided tour have more educational benefits to the visitor than an unguided tour? Question 2: Will a model created and tested in a virtual environment run properly in a real museum?

2. Resources Resources dealing with the personalized museum issues: I read through the following papers which deal with personalized museum issues, such as Bowen (Bowe 2004), Houben (Houben 2009), Waltl (Waltl 2006) and Van Hage (Van Hage, 2010). I was interested in the history of personalization of museum exposition and its development. When surveying currently used aids for museum visitors, I dealt with their adaptability. Starting with the findings outlined in “Visiting with a 'personal' touch” (Fantoni 2002), I built on these findings and added newer data. Table 1: Adaptability of aids for visitors

Adaptive guides

Audio Tours Random/access

Tape Tours

Touch screen kiosk

Books Brochures

Museum Layout

Mobile

Fixed

Location

Mobile

Fixed

Interactivity

Push and Pull

Pull only

Push only

Customisability

Adaptivity

Choice‐based personalization

Multiple tours Multiple tours with the same with the same equipment equipment

Multiple tours with the same equipment

Flexibility

Not interactive Not interactive

Content

Flexible

Fixed

Flexible

Fixed

Multilinguality

Yes

One language at the time

Yes

One language at the time

Cheap

Expensive

Constraints

Expensive; Need to enter Different tapes; Localization Isolated, Must codes; Forced pacing; systems: GPS, be programmed Localization Small info IR sensors, separately. systems storage etc.

In museum practice, printed brochures and audio guides are the most common. As table 1 shows, these are adaptable, but cannot be flexible and are not very appealing to the visitors. Other frequently used aids in expositions are touching screen kiosks. They can serve as a complement to the exhibit or even represent it. The main disadvantages of kiosks are their isolation from other devices (each one requires individual programming) and impossibility to automatically adapt to a visitor. The most customized device for the visitor seems to be a virtual guide (adaptive guide). For that reason I focused my attention on a virtual guide when developing a personalized system. A similar system was studied in 2006 by Bartneck and his team (Bartneck 2007). Their system worked on palm devices (handheld computers, forerunners of today’s smartphone). Even in its time, this system was effective and it can be concluded that with the use of modern technology it will be even more. I drew some inspiration from projects dealing with e‐learning personalization based on the learning styles of students (Kostolányová et al. 2012). The work deals with the most appropriate form provided learning material based on user characteristics.

609

Lukas Najbrt

Figure 1: Model of adaptive learning environment (Kostolányová et al. 2012) (DMA, DMU, DMS = datamaning from autor, teacher, student) In order to create a useful and effective museum exhibition, all its creators, designers and curators have to be well acquainted with the target group. Without understanding the target audience the exhibition cannot succeed because it will not be able to communicate with and foster the interest of visitors. The spectrum of museum visitors is very diverse and there is no general and universal classification. Visitors, however, have some common features upon which we can build our categorization:

socio‐demographic characteristics: age, sex, occupation, education, the type of community the resident is from, local or non‐local residents;

museological characteristics: motivation for the visit (professional, informational), knowledge of the topic, potential of the tour to engage;

range characteristics: individual visitor, (various types of) groups of museum visitors, frequency of visits, timescale of museum visit;

psychological or physiological characteristics: reception, intelligential, memory, imaginative, visual, auditive, motoric

The following table shows different approaches to visitor categorization based on various criteria. Table 2: Examples of visitor categories listed by given criteria Visitors’ differences

Learning styles (McCarthy) (McCarthy 2006)

Learning styles (Gardner) (Gardner 1999)

Visiting styles (Veron and Levasseur) (Veron and Levasseur 1991) Level of expertise

Examples Imaginative = learn by listening and sharing and prefer interpretation that encourages social interaction Analytical = prefer interpretation that provides facts and sequential ideas Common Sense = like to try out theories and discover things for themselves Experiential = learn by imaginative trial and error Linguistic = written material Logical‐mathematical = diagrams, schemes Spacial = maps Musical = audio, music Bodily = manipulation Interpersonal = social context Intrapersonal = alone Ant = interest in all objects following the curator’s path Fish = holistic view Butterfly = interest in all objects without following a specific path Grasshopper = interest only in specific objects Experts Students

610

Lukas Najbrt Visitors’ differences

Examples Tourists Children Historical Artistic Technical Scientific Aesthetic, etc Individual Visitor Group of students/children Family Couple (adults) Couple (adult‐child) Local European American Asian

Type of interest

Social Context

Origin

3. The concept of virtual guide model At the beginning of creating the concept of the virtual guide system, I was concerned with its openness. The whole system should be able to function in a virtual as well as in the real environment where the range of museum expositions is extensive. (The term museum can be understood as, for example, a zoological or botanical garden, art gallery or even a technological park.) It can even serve as a simulation tool in the design stage of a new exposition. For this reason I decided for modular structure of the whole system which will guarantee its flexibility and will allow future integration into other museum systems, especially into the existing database of exhibits.

Figure 2: The system of a virtual guide The whole system consists of three modules:

3.1 Visitor module This module is a diagnostic expert system based on test questions which determine the type of visitor. I was not able to find any universally accepted system of visitor differentiation since there are various ways of classifying them (e.g., based on educational styles, level of expertise, interest in presented expositions or social context). Using a synthesis of these categories and bearing in mind the given conditions for its initial use, I created my own classification of visitors. Since my goal is a fully individual tour, and since testing will be performed in an environment of a virtual museum, I can for the time being exclude the classification of all groups and work with individual visitors only. For the needs of the diagnostic expert system, I specified a hierarchic model breakdown of visitors.

611

Lukas Najbrt Table 3: Classification of visitors Context